Streaming Lower Quality Video over LTE : HowMuch Energy Can You Save ?

Azeem Aqil∗, Ahmed O. F. Atya∗, Srikanth V. Krishnamurthy∗, George Papageorgiou∗∗University of California, Riverside,

{aaqil001, afath001, krish}@cs.ucr.edu, gpapag@ieee.org

Abstract—Streaming video content over cellular con-nectivity impacts the battery consumption of a client(e.g., a smartphone). The problem is exacerbated whenthe channel quality is poor because of a large numberof retransmissions; moreover, streaming high qualityvideo in such cases can negatively impact user experi-ence (e.g., due to stalling). In this paper, we developan analytical framework which can provide the userwith an estimate of “how much” energy she can saveby choosing to view a lower quality stream of the videoshe wishes to view. The framework takes as input thenetwork conditions (in terms of packet error rate orPER) and a coarse characterization of the video to beviewed (slow versus fast motion, resolution), and yieldsas output the energy savings with different resolutionsof the video to be viewed. Thus empowered, the user canthen make a quick, educated decision on the version ofthe video to view. We validate that our framework is ex-tremely accurate in estimating the energy consumptionvia both simulations, and experiments on smartphones(within ≈ 5% of real measurements). We find thatswitching to a lower resolution video can potentiallylead to ≈ 418 mW (23.2%) decrease in the consumedpower for slow motion video, and ≈ 480 mW (26%)for fast motion video in bad channel conditions. Thistranslates to an energy savings of 376.2 J and 432 Jrespectively, for video clips that are 15 minutes long.

I. IntroductionCurrent reports indicate that video streaming to smart-

phones is experiencing an unprecedented growth [1]. Theemergence of LTE (Long Term Evolution standard) [2],which offers significantly higher throughput compared tothe previous generations of cellular networks, has fosteredthis growth. Streaming video over a cellular network how-ever impacts the battery consumption of a client device.While User Equipment (UE) and specifically smartphones,have grown in complexity with better displays and fasterCPUs, the battery technology has not been able to keepup. LTE, due to its ability to sustain significantly higheruser throughput compared to 3G, exacerbates the bat-tery problem during video downloads, since the higherdownlink/uplink data rate translates to higher energyconsumption [3]. It has been shown that the radio interfaceis a significant power consuming resource [3].

Today, adaptive bit rate streaming has become a com-mon practice for streaming video [4]; by detecting theuser’s bandwidth, the quality (resolution/bit rate) of thevideo stream is adjusted so as to improve the user’s quality

Vide

o Se

rver

User Equipment

Our Analytical

Framework Use

r In

terfa

ce

Video Metadata: Duration, Resolution, Slow Vs. Fast Video Characterization

Channel Quality Estimate (PER)

Provide Quality Versus Energy Trade-off

Download Chosen Quality Video

CPU  Processing Energy

Fig. 1: Applicability of our framework.

of experience. However, to the best of our knowledge,changing the quality of the video to lower the energy con-sumption on the user’s smartphone has not been previouslystudied. In particular, a user may choose a lower qualityvideo stream, even when bandwidth/CPU resources areadequate, to reduce her battery drain. We seek to explorethis dimension in this work.

As one might expect, a user can decrease the energyconsumed on her smartphone when downloading a video,by downloading a lower quality version of the same video.In some cases (poor channel conditions), downloading alowered quality video could even improve user experience(prevent stalls); in fact, there has already recent workthat advocate the use of lowered video quality (albeitin a wireline setting) to enhance user experience duringdownloads [5]. However, today there do not exist anytools that allow the user to get an estimate of how muchenergy she can save by choosing a lower quality video fordownloads over cellular connectivity. Such an estimationis intricately hard because of the following reasons: (i)The estimation has to be made without downloading anyof the versions of the video; in other words, it has to bebased on a set of parameters that characterize the videoto be downloaded; (ii) The savings from downloadinga lower quality version would depend on how the UEstate transitions (described later) [2] are affected by thearriving video traffic. This in turn would depend on thechannel conditions perceived by the user at that time.These challenges essentially require that any frameworkmust be holistic and tie in the interactions between thevideo flow characterization, the LTE scheduler and theenergy transitions due to traffic arriving at the UE.

Goal and Vision: In this paper, we seek to developan analytical framework which takes as inputs, factorsthat influence energy transitions at the UE (i.e., videocharacteristics, channel conditions) and yields as outputan estimate of the energy savings possible with a loweredquality video download of a given stream. A pictorial rep-resentation of how our framework can be applied is shownin Fig. 1. Either the UE or the video server can performa small set of calibration measurements to estimate thechannel quality in terms of PER. Alternatively, a modelthat maps the signal strength to PER could be used toestimate the PER at the UE side. The video server wouldprovide metadata [6] from the video clip chosen for down-load in the form of resolution, a coarse characterizationof slow versus fast video (using tools such as AForge [7])and the duration of the video. The UE will also locallyestimate the energy required to process the received videoframes for the different versions of the video. Our modelyields as output an estimate of the energy consumed onthe network interface with the different resolutions of thevideo, the user seeks to view. This combined with the theprocessing power provides the user with an estimate ofthe total energy with the different versions. She can thenmake an educated decision on the version of the video todownload from the server.

Contributions: As our primary contribution we builda mathematical framework to capture the interactionsbetween video traffic, the LTE scheduler, and the energystate machine at the UE. In addition to being useful fornear real-time estimation of energy savings from choos-ing lowered quality videos for downloads, it provides afundamental understanding of how and why different in-put factors influence energy consumption. In essence, theframework considers a general model of video traffic, andcharacterizes the arrival process of video packets at a clientdevice (UE) after they traverse an LTE-based wirelesslink. The arrival process in turn calibrates the transitionsbetween the different energy states in which the UE canreside, and the likelihood of being in each of those states.We validate our framework via extensive simulations andthrough experiments on a real smartphone in a variety ofscenarios, thereby demonstrating its accuracy (the resultsare within ≈ 5 % of the real measured values) as well asgenerality.

Some interesting insights arising from our work are:

• Choosing a lower quality video stream incurs a smallpenalty in terms of the video PSNR (Peak Signal toNoise Ratio) but results in significant energy savings;specifically, a PSNR reduction of 10.1% can fetch energysavings of the order of 375 J for a video of duration15 minutes. When a user views videos over extendedperiods, the energy savings can therefore be significant.

• While as expected, streaming higher resolution videosresult in higher energy, the increase depends on whetherit is fast or slow motion video. For example, in goodchannel conditions, moving to a lower resolution froma higher resolution results in a 480 mW (≈ 26 %)reduction in power (energy consumed per unit time) forfast motion video, but a 418 mW increase (≈ 23 %) forslow motion video.

• In poor channel conditions, moving to a lower resolutionresults almost in identical power savings for slow andfast motion video (≈ a 19.5 % decrease in power).However, the savings in milliwatts is higher for fastmotion video.• For typical video transmissions, one observes that the

time spent in some of the LTE energy states is insignif-icant regardless of the resolution.

Scope: Our framework primarily accounts for the en-ergy consumed by the network interface on the UE. Inaddition, there is a processing energy consumed on a devicefor processing/playing back the video frames; this energyis device dependent and we use empirical results that aredriven by experiments (this can be measured locally onany device).

For validation, we assume that videos are streamed us-ing fixed bit rates. However, our framework can be appliedto adaptive bit rate streaming as discussed in Section VI.We employ video resolution and PSNR as the metricsfor quantifying video quality. It has been shown that theperceived video quality on mobile devices is affected bythe size of the screen, user mobility, and ambient light [8].Accounting for these factors is beyond the scope of thiswork and will be considered in the future. For analyticaltractability, we also assume that the user is stationaryduring the course of video streaming.

While we validate our framework via simulations andexperiments, we do not implement the complete systemshown in Fig. 1. To implement such a system, we will needto make changes to the video server so as to deliver theappropriate metadata to the client UE, and also have adynamic PER estimation tool for the LTE link; these arebeyond the scope of this paper and will be considered infuture work.

II. Relevant BackgroundIn this section, we describe aspects of LTE that we seek

to capture in our analytical framework.The Radio Resource Control (RRC) State Ma-

chine: The LTE RRC state machine captures the differentenergy states that a UE can be in and has two primarystates: rrc idle and rrc connected. The latter hasthree modes as shown on the right side of Fig. 2. If the UEis in rrc idle, then any data exchange (even corrupted)triggers a transition to the rrc connected state. The UEthen enters the continuous reception mode and monitorsthe physical downlink control channel (PDCCH), on whichcontrol information is delivered from the base station(referred to as enB in LTE jargon [2]). At this time, theUE also starts its continuous reception timer, Tc. If nopackets are received before the expiry of this timer i.e.,in Tc, the UE enters the Short DRX mode. In this modethe UE alternates between ON and OFF periods (calledDRX cycles) to save energy. If during any ON period, theUE receives data or has data to send, it returns to thecontinuous reception mode.

Upon entering the Short DRX mode, a different timer,Ts, is set. If there is no data transfer (received or sent)

prior to the expiry of this timer, the UE enters the LongDRX mode. The Long DRX mode is similar to the ShortDRX except that it has longer DRX cycles and a biggertimer value (Tl) associated with the time prior to exitingthis state. Thus, Ttail = Tc + Ts + Tl represents time forwhich no packets should be either received or sent in orderto return to the rrc idle state, and is referred to as theLTE tail period.

Packet transfers in LTE: The transitions betweenthe states in the LTE RRC state machine are dictatedprimarily by packet receptions during video streaming.This in turn is handled at the MAC (and the PHY) layerof the LTE protocol stack. Our focus in this work is on theMAC layer, and we abstract the PHY in terms of packeterror probabilities. Thus, we describe this layer in somedetail below. A more detailed description of all the layersof LTE can be found in [2].

MAC and Physical Layers: The MAC layer im-plements a Hybrid Automatic Repeat reQuest (HARQ)protocol for reliable packet transfers. It also dynamicallyselects the Modulation and Coding Scheme (MCS) tobe used at the PHY, based on the channel conditions.Transmissions in LTE are organized into frames that are 10ms long. Each frame is divided into ten 1 ms subframes.The LTE transmission time interval (TTI) specifies thegranularity at which packets are scheduled and this is doneonce every subframe (TTI = 1 ms).

The HARQ process: The HARQ process is differentfrom a regular ARQ process in how it sends data and re-transmissions. In LTE, incremental redundancy and chasecombining are both supported. With these approachesretransmissions add required redundancies and are com-bined with prior transmissions to increase the likelihoodof packet success.

LTE uses multiple HARQ processes simultaneously.The number of these processes, N , are chosen such thatit is greater than the round trip time (RTT) of a singleHARQ process (in time slots). The multiple processestransmit packets one after the other (round-robin), as acontinuous stream without waiting for acknowledgments(ACKs) or negative acknowledgments (NACKs). Each pro-cess will have received an ACK or a NACK by the timeit is its turn to transmit again. The number of HARQ

P

Video Process

RRC State Machine

Module B

Continuos Reception

Short DRX

Long DRX

IDLE

Tc

TsTl

Que

ue

12

3

NpHybrid - Arq processes

+ Channel Conditions

Module A

Distributer

Fig. 2: A depiction of the system considered in our framework.Module A represents the LTE scheduler and Module B repre-sents the UE RRC machine. The output of Module A, which isthe probability of a packet being sent in a TTI, p, is the inputfor Module B.

processes for FDD (frequency division duplexed) LTE is 8(the RTT with LTE is typically < 8 ms [2]). The number oftransmission attempts that a single HARQ process makesbefore dropping the packet is generally 5, i.e., it makes 1transmission and 4 retransmission attempts.

III. Our Analytical FrameworkIn this section, we build our mathematical framework

for understanding the trade-offs between video quality andthe battery consumption at the UE. As expected, the UEenergy consumed will decrease if one were to lower thequality of the video that is downloaded. However, thesavings will depend both on the type of video (resolution,slow versus fast video) as well as the channel conditions(poor versus good link quality).

To reiterate, we envision our framework to be used asdepicted in Fig. 1. Prior to sending a video stream, thesender characterizes the video in terms of its type (slow orfast motion) and resolution. The sender and the receiveralso perform a set of calibration measurements to estimatethe link quality in terms of the PER. These parametersare then input to our framework, which then outputs theexpected energy consumption at the UE for a download ofa particular duration.

We first model the video input process. Subsequentlywe characterize how these video packets are processedby the LTE scheduler and thereby affect the transitionsbetween the energy states at the UE. The notation used issummarized in Table I.

A. Video InputThe input process characterizes the arrival of video

packets into the LTE buffer. We assume that the video iscomposed of I, P and B frames 1. A segment correspondingto an I frame is typically much larger than the MTU(maximum transmission unit) of the network and mustbe fragmented into multiple packets. The I frames arealso less frequent than the much smaller P or B frames.The P and B frames are typically smaller than the MTUsupported by the network. Since in terms of size, P andB frames are similar, we do not distinguish them in ourmodel. In essence, we seek to capture two different phasesof arrival which correspond to that of I frames and P/Bframes, respectively. A natural choice for such a setup isthe Markov modulated Poisson process (MMPP), whichrepresents a doubly stochastic Poisson process [10]. Thefirst state of the MMPP represents the arrival of I framesand the second, the arrival of P/B frames. The rate oftransition from state 1 to 2 is r1 and that from state 2to 1 is r2. When the process is in state 1, packets thatare generated due to I frames arrive at a rate λ1; in state2, packets generated due to P/B frames arrive at a rateλ2. The process can be represented by the infinitesimalgenerator R and the rate matrix Λ, given by:

R =[−r1 r1r2 −r2

], Λ =

[λ1 00 λ2

](1)

1For details on video representations please see [9].

Parameter Descriptionπmmpp Steady sate vector of the 2-MMPP video processR Infinitesimal generator for the 2-MMPP video processΛ Rate Matrix of the 2-MMPP processp The probability of receiving something in a given TTINp The number of HARQ processesr Maximum number of transmission attempts of a single HARQ processπharq The steady vector of a single HARQ processπrrc The Steady state vector of RRC state machine Markov Chain

TABLE I: Key parameters in our mathematical framework

Fig. 3: Pixel Density (Resolution) VS Bit rate

The steady state vector πmmpp ( which represents theprobability of being in state i, i ∈ {1, 2} ) is given by

π = (π1, π2) = 1r1 + r2

(r2, r1) (2)

The expected arrival rate of the 2-MMPP process, λavg,is given by πλ, where λ is the column vector with thediagonal elements in Λ, i.e., λ = Λ.e, where, e = (1, 1)T .

Parameterizing video quality: In order to use theabove representation we need to map the given videoquality to the parameter λavg (this parameter influencesthe energy consumed as we will see later). Videos can becategorized as fast or slow motion videos [7]. Fast motionvideo is characterized by successive video frames that havelittle in common while slow motion video is characterizedby successive frames that have a lot in common. Accord-ingly, fast-motion videos consume more bits in encodingthan slow-motion videos. If the effective bit rate is known(Bitrate), λavg ≈ Bitrate

E(P acket Size) .

If the video server can provide information with regardsto the bit rates associated with different versions of thevideo clip as metadata, λavg can be readily computed. Onecan also empirically compute λavg from the resolution andthe category of the video (slow or fast motion) if the bitrate is not readily available as follows.

The resolution of a video stream is essentially a functionof the number of pixels per frame; the higher this number,the higher the resolution. To map the resolution to thebit rate, we perform measurements across 5 video streamsfor each type of video (fast or slow). We find that thebit rate is directly proportional to the resolution of thestream as shown in (shown in Fig. 3). The proportionalityindex depends only on whether the video is of fast or slowmotion. With such a characterization (fast versus slow), wefind that the prediction error is < 5%. Thus, given a certainvideo type and its resolution, the server can determinethe average arrival rate of packets to the MMPP process(using these offline measurements). Stated otherwise, the

quality of the video in terms of its resolution can be usedto characterize its arrival process to the LTE scheduler.

Fig. 3 demonstrates this relationship for each type ofvideo. From the figure we can directly infer the decreasein λavg when the resolution is changed.

B. Modeling LTE effectsNext, we characterize the behavior of the RRC state

machine at the UE given (i) the type of video beingstreamed and its resolution, and (ii) the state of thechannel. The system has two distinct parts as shown inFig. 2. The first part (Module A ) reflects the process oftransfer of the video traffic over LTE to a specific UE.The second part (Module B) characterizes the RRC statemachine at the UE. The overall objective here is to deter-mine the expected time spent in each of the RRC states.Module B essentially takes as input p, which representsthe probability of receiving a packet (either a decodablepacket or a corrupted packet) in a TTI. This probabilityessentially depends on the arrival process of the video flow,the functionality of the LTE scheduler (Module A) and thechannel quality of the wireless link.

Assumptions: We make the following assumptions foranalytical tractability. First, we assume that the UE isrelatively stationary and thus, the channel conditions donot change (slow fading) for the duration of a transmission.However, we assume that they can vary between transmis-sions due to fading, and thus the likelihood of a packetsucceeding in a transmission attempt is independent ofwhat happens in other attempts. Second, we ignore syn-chronization issues. Note that we are only interested inthe average energy due to the UE being in a state and notthe transient energy behaviors while in a state (e.g., weonly account for the average energy because of being inthe Short DRX state).

To begin with we assume that the UE in question isscheduled every TTI. We relax this later to account forthe possibility that it gets scheduled once every Np TTIs,on average.

Packet processing at the LTE transmitter: Thepacket processing at the LTE transmitter consists of twoparts. First, we have an MMPP/G/1 queue to which thevideo packets arrive. The server of this queue essentiallyacts as a distributor and places the packets in one of Np

HARQ processes. Note that in order for the distributor toplace a packet, at least one of the HARQ processes must beempty. Next, the HARQ process delivers the packet to theUE. First, we determine the service time distribution forour MMPP/G/1 queue and thereby compute its utiliza-tion. We later discuss how we map this to the probabilityof Module B receiving a packet in a TTI.

Characterizing the service time: The service timeis influenced by the functions of the LTE HARQ processes.We denote it by S, which is essentially the time it takes fora packet to be assigned to a HARQ process by the distribu-tor. We first describe how a single HARQ process functionsand then discuss how we determine the distribution of Sconsidering the Np processes together.

1 2 3 rp1 p2

p3 pr-1

1 - p2

1 - p3

1

1 - p1

Fig. 4: Markov Chain representing a single process hybrid-arq.Each state i, i ∈ {1, 2...r} represents the transmission attemptand r is the maximum number of transmissions allowed.

Model of a HARQ process: We model a single HARQprocess as a finite state discrete time Markov chain asshown in Fig. 4. The number of states, r, corresponds tothe maximum number of transmission attempts allowed(after which the packet is dropped). The initial state (state1) refers to a state wherein the HARQ process receivesa new packet from the distributor. pi is the probabilityof a packet being received by the UE in error, while instate i. A successful transmission while in state i, occurswith probability 1 − pi, and results in a transition to theinitial state (state 1) and the process gets the next newpacket. Because of additional FEC or change to a lowerMCS (this is how HARQ functions), later re-transmissionshave a greater chance of success, i.e., pi ≥ pi+1

2. It is easyto see that the transition probability matrix for the Markovchain, P , is thus:

P =

1− p1 p1 0 · · · 01− p2 0 p2 · · · 0

......

.... . . 0

... 0 0 · · · pr−11 0 0 · · · 0

(3)

The steady state probability vector, πharq = (π1, π2, ..πr),is then computed by solving the equations πharq.P = Pand

∑i π

harqi = 1. Specifically,

πharq1 = 1

1 +∑r

i=2∏i−1

j=1 pi

, πharqk,∀k>1 = πharq

1

k−1∏i=1

pk

(4)Joint consideration of the HARQ processes: To derive

the service time distribution, we need to characterize howthe Np HARQ processes function together as a whole.To recap, the Np processes are served in a round robinfashion. In every TTI, there is only one HARQ processthat is scheduled for transmission. Since Np > the RTT ofin terms of TTI’s, each transmitting process will receivean ACK or NACK by the time it is its turn to transmitagain.

Deriving S: The distributor assigns the packet at thehead of the queue to next available HARQ process. Thetime taken for this assignment, S, is essentially the timeit takes for the distributor to find a free HARQ process.

Consider a packet, k, that is to be next assigned to oneof the HARQ processes. For k to experience the “max-imum assignment time”, Smax, all the HARQ processes

2 In the NS3 LTE simulator we use [11], we see that all the pis arenearly equal although this relation holds in general.

(r-­‐1)  TTI  periods  

Np    HA

RQ  processes  

Prior  packet  scheduled  here  

Process  k,  TTI    j  

Latest  ;me  by  which  tagged  packet  is  scheduled,    

k=1,  j=0  

Fig. 5: The state of the LTE HARQ processes

must be occupied for the maximum possible durationi.e., each process must perform the maximum number of(re)transmission attempts after k reaches the distributor.Smax, is then, the sum of (i) one TTI for the initialtransmission of packet k−1 and (2) the r−1 transmissionattempts made by each of the Np processes subsequently(each of these processes must be occupied by a packet andmust have at least performed one unsuccessful attemptalready; else at least one would be empty and packet kcan be assigned). It is easy to see that Smax is thus givenby: Smax = (r − 1)Np + 1.

To aid the discussion on the calculation of S, we referto Fig. 5. The rows in the figure correspond to the HARQprocesses, while the columns correspond to time in termsof TTIs. Without loss of generality, assume that the priorpacket that was assigned by the distributor, was assignedto the last HARQ process viz., process Np; in the figurethis preceding packet (relative to a tagged packet that weconsider) is assigned to the black TTI (we refer to eachblock in the figure as a “slot” from here on). At this point,the tagged packet enters the distributor.

Let us assume that the tagged packet is assigned toa HARQ process, jNp + k slots later (j ∈ {0, r − 1}, k ∈{1, Np}). This corresponds to the shaded slot in the figure.For this to happen, the following must hold true: (i) Theprocesses from 1 to k−1, must be occupied for j+ 1 slots;(ii) The processes from k+1 to N−p must be occupied forj slots; and, (iii) The process k must be occupied for j slotsand must be free in the (j+1)st slot. The probability of thetagged packet assigned as above is given by Equation 5; inthe following, we elaborate on how we arrive at this result.For simplicity, we simply refer to πharq

l (recall Equation 4)as πl.

Let us first consider the simple case wherein j = 0.If the process to which the packet is assigned (processk) is one of the first (Np − 1) processes (not the one towhich the previous packet was assigned to), the conditionsthat must be satisfied are (i) the preceding k−1 processesmust have been occupied and the kth process is free. Thelikelihood of this is (1 − π1)(k−1)π1. If the process inquestion is the last (N th

p ) process, the requirement is thatall the previous processes were occupied and the previouspacket (transmitted in the black slot) was successful. Thelikelihood of this is (1− π1)(k−1).(1− p1).

Next, let us consider the case where j 6= 0. To

P (S = jNp + k) =

(∑r

l=j+2+1 πl)(k−1).(∑r

l=j+2 πl)(Np−k−1).∏j

m=1 pm).(∑r−1

l=j+2 πl(1− pl) + πr) j > 0, k 6= Np

(∑r

l=j+2+1 πl)(k−1).∏j−1

l=1 pl(1− pj) j > 0, k = Np

((1− π1)(k−1).π1 j = 0, k < Np

(1− π1)(k−1)(1− p1) j = 0, k = Np

(5)

begin with let k 6= Np. The probability of thefirst condition in the aforementioned list holdingtrue for each of these processes, is essentially:π2.p2.p3 . . . pj+2+1 + π3p3.p4 . . . pj+3+1 + . . . · · · +π(r−1)−(j+1).p(r−1)−(j+1) . . . pr−1. Using Equation 4it can be shown that this long expression is simply∑r

l=j+2+1 πl. Together, for the k − 1 processes (assumingthat they are independent because of varying channelconditions between transmissions from these processes,due to fading), the probability of the first event is(∑r

l=j+2+1 πl)(k−1).

Similarly, (∑r

l=j+2 πl)(Np−k−1)).∏j

m=1 pm gives us theprobability of the second event. The last term correspondsto process Np to which the packet preceding the taggedpacket was assigned. For this packet, it is known that atransmission attempt was made in the black slot, and thelast term accounts for this.

Finally, let us consider the the last required event.Since, process k is one of the first (Np − 1) processes, thisevent occurs with a probability

∑r−1l=j+2 πl(1 − pl) + πr.

This essentially corresponds to failed attempts in the firstj slots followed by either a packet success or a packet dropfor that process (k) in the jth slot.

If k = Np, things are slightly different since we knowwhen the previous packet was scheduled. Thus, the likeli-hood of this process being free for the first time at the jth

TTI is simply∏j−1

l=1 pl(1− pj) for 0 < j ≤ r − 1.Determining the likelihood of a packet reception

in a TTI: Having characterized the arrival and the serviceprocesses, we next determine the likelihood of a packetreception (either decodable or corrupted) in a TTI at theUE. A reception either transitions the UE to the activestate or keeps it in one of the composite modes in thatstate.

The UE receives a packet in a TTI if the correspondingprocess has a packet to send. If not, there is no reception.Here, we make the following approximations. If the queueis non-empty it is unlikely that any of the Np processesis empty and thus, the UE will receive a packet in eachTTI. If the queue is empty and N of the Np processes areoccupied, the likelihood of the UE receiving a packet in aTTI is:

β =Np∑

N=1

N

NPP (No of Pkts in System = N). (6)

The probability of the MMPP/G/1 queue being non empty

is simply given by:

ρ = λavgE(S) (7)

Thus, the probability of the UE receiving a packet in aTTI is given by:

p = ρ+ (1− ρ)β. (8)

If ρ is high, the second term in Equation 8 tends tozero. On the other hand, if ρ is small, the value of Nand thus, β is even smaller (meaning that if the queueis empty, the likelihood that some of the processes areoccupied is very small). Thus, we ignore the second partand approximate p ≈ ρ. We later validate that thisapproximation is reasonable via simulations (where wedon’t make this assumption).

C. Impact on the LTE RRC State MachineNext, we seek to capture the impact of p on the

LTE RRC finite state machine (FSM). We model thestate machine as a finite-state discrete-time Markov chainparametrized by p, the probability of receiving a packet ata given TTI. The Markov chain is shown in Fig. 6. Initiallythe chain is in the idle state. A packet reception (withprobability p), results in a transition to the continuousreception state (consisting of states 1 through Tc). A statetransition from a higher RRC power state to lower stateoccurs if no packet is received (with probability 1 − p)for Ti, where Ti reflects the timer value associated withthe currently occupied RRC state. Any packet receptiontriggers a timer reset and the machine transitions to thecontinuous reception state if in any other occupied state.The transition probability matrix for this FSM, P , is givenby

P =

1− p p 0 · · · · · · 00 p 1− p 0 · · · 0... p 0 1− p 0 0...

......

.... . . 0

0 p 0 · · · · · · 1− p1 p 0 · · · · · · 0

(9)

The steady state probabilities πrrc of being in eachsub-state depicted in the figure is then computed using:

πrrcP = P (10a)∑i

πrrci = 1 (10b)

IDLE12Tc

Tc +2

Tc + Ts

Tc + Ts +1

Tc + Ts + 2

Tc +1

Tc + Ts + Tl

Continuous Reception

Short DRX Long DRX

Fig. 6: Markov chain representing the LTE RRC state machine.State 0 corresponds to the Idle state. States 1 through Tc rep-resent the continuous reception state. Dotted and solid arrowsrepresent transitions with probability p and 1− p, respectively.

To calculate the probability of each individual RRC statewe can simply sum the probabilities of being in any of thecomponent sub-states of that state (i.e., πrrc

i ’s that corre-spond to sub-states i within that state). From equations10 one can compute the following:

πrrcIDLE = (1− p)Ttail (11a)

πrrcRRC Connected = 1− πrrc

IDLE (11b)πrrc

ContinousReception = 1− (1− p)Tc (11c)πrrc

ShortDRX = −(1− p)Tc((1− p)Ts − 1) (11d)πrrc

LongDRX = (1− p)Tc+Ts − (1− p)Ttail (11e)

where Ttail = Tc + Ts + Tl. Given the expected energyconsumed in each TTI when being in each of the statesof the RRC state machine, and the specifications of avideo stream (resolution, slow versus fast) and its duration,one can now compute the expected energy consumed bynetwork interface from the download.

Energy due to packet receptions: We wish to pointout that while being in each of the RRC CONNECTEDstates results in a baseline energy expenditure, there isadditional energy consumed when packets are received [3].This increase is directly proportional to the rate at whichpackets are received while in that state. This is especiallyimportant for the continuous state, since increased rate ofpacket receptions leads to this state almost inevitably. Insuch conditions, since ρ linearly increases with rate, theincrease in energy is linearly proportional to ρ.

D. Impact of LTE SchedulingThus far, we assumed that the tagged UE is scheduled

every TTI. However, depending on the number of usersand the provider’s policy, this may not be true. In betweentransmission attempts, there maybe TTIs where the UEis not scheduled, and this results in an additional servicetime component, viz., a waiting time wherein, no process isactive and the distributor is simply in a wait mode. If theUE is scheduled every W slots (W is a random variable)and if we assume that this is independent of the servicetime rendered to a packet3, the expected service time is

3This is reasonable since the external load is independent of whatthe UE is attempting to download.

0 1000 2000 3000 4000 50000

50

100

150

200

250

300

Bit Rate

Po

wer

(m

Wat

t)

HTC M7HTC M7 Linear RegressionLG G FlexLG G Flex Linear RegressionGalaxy S4Galaxy S4 Linear Regression

Fig. 7: Bit Rate Vs CPU power

scaled up by a factor E(W ) due to this scheduling policy.Thus, the probability that the queue is non-empty is nowρ = ρE(W ).

The likelihood of the UE receiving a packet in a TTIdepends on whether or not the UE was scheduled in thatTTI; the probability of the UE being scheduled in a TTI isq = 1

E(W ) (we assume that in general E(W ) is small; if not,the UE is not scheduled for long durations and the queuemay become unstable). If scheduled, a packet is receivedwith probability ρ. Thus, the probability of receiving apacket in any arbitrarily chosen TTI is ρ.q = ρ. Thus, inessence, our prior analysis applies in this more general caseas well. We have validated this via simulations.

E. Power consumption due to processingThe power consumed at the UE includes the power

due to the processing of the received frames (decodingand playing the video) in addition to the power consumedon the (LTE) network interface. Our model only explic-itly accounts for the latter. Higher resolution videos aretypically larger in size and thus require higher process-ing/computing resources (and therefore power) at the UE.

The power consumed for processing is device dependent(i.e., it depends on the CPU and battery of the device). Ifthe bit rate of the video stream is known (can be providedby the video server), the UE can locally estimate the pro-cessing power that will be consumed in addition to the LTEinterface power discussed thus far. To show the viabilityof this approach, we experimentally profile the CPU powerconsumption for 3 different smartphones (Samsung GalaxyS5, HTC One M7 and LG G Flex) for downloading videoswith different bit rates using the PowerTutor tool [12].The results from our experiments are plotted in Fig. 7.We see that, for each phone, the power consumed due toCPU activities (processing/playback) increases (roughly)in a linear fashion with the bit rate; thus, the local devicecan easily estimate this power for different bit rate videoclips once it does an initial calibration with a few videodownloads.

Combining this CPU power with the estimated powerconsumed on the network interface (obtained using ourmodel), we are able to get a estimate of the total powerconsumed by different versions of a video stream.

Resolution Types EGA, CGA, HD, FHDStreaming server DarwinEncoding Protocol MPEG4Wireless Device Samsung Galaxy s5

TABLE II: Experimental Setup

Type Protocol Resolution Fast Motion (Kb/s) Slow Motion (Kb/s)CGA h.264 320x200 293 111EGA h.264 640x350 802 236HD h.264 1280x720 2433 619FHD h.264 1920x1080 4656 1174

TABLE III: Details of types of videos used

0.1 0.2 0.3 0.41300

1350

1400

1450

1500

Packet Error Rate (p1)

Po

wer

(m

Wat

t)

ModelSimulation

Fig. 8: Power consump-tion versus PER for slowmotion, low resolutionvideo

0.1 0.2 0.3 0.41000

1200

1400

1600

1800

Packet Error Rate (p1)

Po

wer

(m

Wat

t)

ModelSimulation

Fig. 9: Power consump-tion versus PER for slowmotion, high Resolutionvideo

0.1 0.2 0.3 0.41300

1350

1400

1450

1500

1550

1600

Packet Error Rate (p1)

Po

wer

(m

Wat

t)

ModelSimulation

Fig. 10: Power consump-tion versus PER for fastmotion, low resolutionvideo

0.1 0.2 0.31000

1200

1400

1600

1800

2000

Packet Error Rate (p1)

Po

wer

(m

Wat

t)

ModelSimulation

Fig. 11: Power consump-tion versus PER for fastmotion, high resolutionvideo

IV. EvaluationsIn this section, we validate our analytical framework

via simulations and experiments. We consider differenttypes of video, and vary channel conditions to understandhow video downloads affect the UE energy consumption.We first describe our simulation and experimental setups,discuss how we compute energy with our model, and laterdiscuss our results.

A. ConfigurationsSimulation settings: We use NS3[13] in combination

with the LTE module developed by the LENA Project[11].We use the default channel model used in this project.Details can be found in [11]. Since we are only interestedin the energy consumed at the UE, we set up a simple LTEtopology with one enB serving a single UE. We log all thePHY packets that the radio processes (including packetsthat are in error). We vary the channel conditions. We use8 different video clips each of which is about 60 secondslong. Videos are tagged and processed using the EvalVid[14] tool and then passed on to the enB to be transmittedto the UE. For each experiment, we perform 20 simulationruns.

Experimental Setup: Our experiments are on aSamsung Galaxy S5 LTE phone over the AT & T LTE

Network (we did experiments with other models and T-Mobile and the results were similar). We connect our LTEphone to a Monsoon Power Monitor [15] and measure thepower when different types of video (discussed next) arebeing downloaded from a Darwin server. We collect powertraces for each resolution of fast and slow motion videos10 times and estimate the average power. The details ofour experimental set up are in Table II.

Video: We use four popularly used video resolutionslisted in Table III. For each video, we translate the res-olution into a bitrate, and estimate λavg as discussed inSection III.

Parameters for our analytical framework: Tocompute the power consumed, we use our analytical modelin conjunction with the results in [3]. Specifically, from [3],we use the following: (a) In the idle state a constant powerof 594 mW is consumed (594 mJ of energy is consumed in1 second). In the continuous state, power = αη+β. Whereα = 51.97 (power/Mbps), β is the baseline power in thisstate and is equal to 1288.04. η is the rate of reception inMbps (and can be computed from ρ). For the Short DRXand Long DRX states, the power alternates between thepower in the idle state and an active power; this activepower is approximately 1680mW (note that this is largerthan the continuous reception baseline power due to thepower due to switching states). For the Short DRX statethe switch from idle to active periods happens every 20ms and for the long DRX state it happens every 40ms.We also use the results from [3] for the timer values thatdictate the RRC state machine transitions. Specifically,Tc = 100 ms, Ts = 20 ms, Tl = 11450 ms. We also set thevalue of all the pis to be the same as p1 as we observed inour simulations that these did not change by much fromp1.

Processing power at UE: The power consumed atthe UE includes the power due to the processing of thereceived frames in addition to the power consumed on thenetwork interface. Our model only explicitly accounts forthe latter i.e., the power consumed due to the LTE inter-face. Higher resolution videos are typically larger in sizeand thus require higher processing/computing resources(and therefore power) at the UE. To estimate the energyconsumption due to processing, instead of reinventing thewheel, we simply use the powerTutor tool [12]. This allowsus to profile and estimate the power consumed by theCPU for the purposes and rendering the video. Combiningthis estimate with the estimated power consumed on thenetwork interface (obtained with our model), we are ableto get a estimate of the total power consumed by differentversions of a video stream.

0.1 0.2 0.3 0.40

0.2

0.4

0.6

0.8

Packet Error Rate (p1)

Pr.

of R

x a

pack

et in

a ti

mes

lot

Slow Motion − ModelSlow Motion − SimFast Motion − ModelFast Motion − Sim

Fig. 12: The probabilityof receiving a packet ina TTI (decodable or cor-rupted) for low resolutionvideos

0.1 0.2 0.3 0.40

0.2

0.4

0.6

0.8

Packet Error Rate (p1)

Pr.

of R

x a

pack

et in

a ti

mes

lot

Low Resolution − ModelLow Resolution − SimHigh Resolution − ModelHigh Resolution − Sim

Fig. 13: The probabilityof receiving a packet ina TTI (decodable or cor-rupted) for high resolu-tion videos

Po

we

r (m

Wa

tt)

Slow Motion Fast Motion0

50

100

150

200

250

300

350

400

450Model

CPU

Experiments

Fig. 14: Power reduc-tion from lowering res-olution from highest tolowest level under goodchannel conditions: Ana-lytical and experimentalresults

Po

we

r (m

Wa

tt)

Slow Motion Fast Motion0

100

200

300

400

500

600

Model

CPU

Experiments

Fig. 15: Power reduc-tion from lowering res-olution from highest tolowest level in bad chan-nel conditions: Analyti-cal and experimental re-sults

B. Results and InferencesSlow motion video: In Figs. 8 and 9, we present both

the analytical and simulation results for slow motion videosof the lowest and highest resolutions. We see that the ana-lytical results match well with those from simulations. Wenotice that at low packet error rates (PER), there is abouta 280 mW (16.9 %) decrease in power when we switch fromthe high resolution video to low resolution video4. As thePER increases, the decrease is more pronounced (19.1 %,340mW). This is because at high PERs, there are increasedretransmissions, which essentially increase the probabilityof being in one of the active states. Furthermore, theincrease in receptions trigger power increases even whenin the continuous mode due to packets in error.

Fast motion video: Figs. 10 and 11 present analogousresults with fast motion video. First, again, the analyticalresults match well with simulation results. We notice thatwith fast motion video, transitioning to a lower resolutionresults in about a 318mW (17.8 %) reduction in the con-sumed power in good channel conditions. In bad channelconditions we see a 384 mW (19.8 %) decrease in powerconsumption. This is because, with this type of video athigh resolutions the packet arrival rate is as is high; theretransmissions further increase the energy consumed. Weobserve here that the UE is mostly in the continuousstate regardless of whether the channel condition is goodor bad. Note here that at p1 > 0.3, the queue becameunstable and we could not gather meaningful results withthe simulations.

4 To lower the video resolution, we switch from FHD to EGA.

Probability of reception in a TTI with slow andfast motion videos: In Figs. 12 and 13, we depict theprobability of receiving a packet (decodable or corrupted)at the UE, in a TTI. As one might expect, the proba-bility increases as the PER increases since there will be ahigher number of retransmission attempts. Further, also asexpected, this probability is higher for fast motion video,and for higher resolution videos, since the bit rates arehigher. Most importantly, we see a close match betweenthe simulation and analytical results; this shows thatthe assumptions made for analytical tractability do notinfluence the results by much.

Comparing analytical results with experimentalresults: In Fig. 14, we compare the results obtained usingour framework, with that from real experiments on ourSamsung Galaxy S5 LTE phone. First, we consider goodchannel conditions (4-5 bar coverage). Since we do nothave access to the LTE PHY interface, we simply map thecoverage level (5 bar) to a rough empirical characterizationof the PER. Specifically, in this case, we set the PER tobe 0.1 as a conservative estimate. The total power withour approach is the sum of the LTE interface power andthe CPU processing power as discussed in Section III-E.We observe that the results derived from our approachare fairly good estimates of the total energy consumedin reality (within 5 % of the measured energy savings).We see that the power savings are significant with bothfast and slow motion video when the user chooses a lowerquality version. The savings are higher with fast motionvideo since, there is a bigger reduction in the video bitrate (≈ 405 mW) as compared to slow motion video (≈335 mW).

In Fig. 15, we plot the results with bad channel con-ditions (1 bar coverage). Here we empirically choose thehighest PER that we could tolerate (in our simulations)without the queue becoming unstable (0.39) when we useour model. The total power saved is the sum of what issaved on the network interface and due to processing. Weobserve again that our results once again match very wellwith the results from the experiments (within 5% of theexperimental results). We find that the savings increase to≈ 480 mW and ≈ 418 mW respectively, for fast and slowmotion video, potentially due to a decrease in the numberof retransmissions.

Finally, in Table IV, we show the decrease in PSNRthat comes with a corresponding decrease in energy for15 minute long videos. Interestingly, a small decrease inPSNR (49.5 dB to 44.5 dB for slow motion and 48 dBto 41.5 dB for fast motion, which corresponds to 10.11% and 14.4 % in the two cases, respectively) results inconsiderable energy savings (e.g., 376 J for slow motionvideo and 432 J for fast motion video in bad channelconditions). The reason for only a slight reduction is thatthe PSNR decreases only due to a reduction in resolution;due to TCP there are no losses that degrade quality on thechannel itself. Thus, by lowering the video quality slightly,the user can conceivably gain significant energy savings.

Impact of LTE scheduling: In Fig. 16, we showthe impact of scheduling the UE once every W TTI onaverage. W is chosen as per a uniform distribution between

1 3 81300

1400

1500

1600

1700

1800

Average waiting time (W)P

ow

er (

mW

att)

ModelSimulation

Fig. 16: Powerconsumption with differentexpected waiting times(E(W )).

0.00 0.01 0.02 0.03 0.04 0.05 0.06

λavg

0.0

0.2

0.4

0.6

0.8

1.0

Pro

babili

ty o

f bein

g in s

tate

i idle

continous

Short Drx

Long Drx

Fig. 17: The change instate occupancy with λavg

CGA EGA HD FHD1350

1400

1450

1500

1550

1600

1650

1700

1750

1800

mW

Fast

Slow

Power consumption for p=0.1

Fig. 18: Comparison inpower consumption withdifferent resolution types.

Decrease in PSNR Bad Channel Good ChannelSlow-Motion 10.11% 376.2J 301.5JFast-Motion 14.5% 432J 364.5J

TABLE IV: The energy saved as resolution is changed fromhigh to low with the corresponding decrease in PSNR

1 and twice the average value. We observe that the powerconsumed does not change by much with varying W . Thisvalidates our analysis in Section III-D.

Times spent in each of the LTE states: In Fig. 17we show the probability of being in each LTE energy state.We see that if λavg increases beyond an extremely lowvalue, the UE is almost never in the IDLE state. As onemight expect, as λavg increases the likelihood of beingin the continuous mode increases, while the likelihood ofbeing in the Long DRX state decreases. To begin, thelikelihood of being in the short DRX state increases; herethe time is shared between this state and the continuousstate. But after a certain point, the probability of beingin this state decreases and the UE is almost always in thecontinuous state.

Power consumption decreases with resolution:For completeness, we present a plot wherein we showthe variations in the consumed power with fast and slowmotion video as we vary the quality in terms of resolution.We assume p1 = 0.1. The difference in power consumptionbetween FHD and CGA are as reported earlier. Interimresolutions offer different trade-offs between power andvideo resolution.

V. Related WorkAlmost all of the power models for LTE are empir-

ically derived. In [3], the authors empirically derive apower model based on an experimental study of LTEperformance. However, the work is mainly intended fordevelopers and does not provide an understanding ofhow the energy consumption varies with different typesof downloads, or in varying channel conditions. In [16],the authors experimentally characterize how variations insignal strength can affect UE power consumption. Theydevelop an approach to schedule communications duringperiods of strong signal strength. However, they do notaccount for traffic characteristics (video) on RRC statetransitions. The authors in [17] study the impact of signalstrength on battery drain and evaluate various energy

saving schemes for 3G networks; however, the impact oftraffic patterns is not considered.

The authors in [18] present a model of the LTE radiointerface. In [19], the authors optimize the LTE timersto save energy. Zhou et al., [20] investigate the trade offbetween saving power and wake up delay. In [21] the au-thors investigate the effects of varying the LTE parameterson user experience. However, none of these efforts arefocused on video, nor do they capture the interactionsbetween the video characteristics and the LTE energystates. In [22], the authors model the LTE HARQ processas a Markov chain to evaluate the energy implications ofthe HARQ process but the model does not capture thecharacteristics of video traffic or how the LTE schedulerinfluences transmissions.

Some of the work that looks specifically at energyconsumption due to video transfers over LTE (e.g., [23]),target the optimization of the LTE timer values to re-duce energy; they cannot be directly applied to determineenergy savings due to lowered video quality. In [24], theauthors study how YouTube traffic affects energy. He et.al. present a model for saving energy for videos streamedover a wireless link in [25]; however, their work focuses onvideo encoding and does not address radio power. Lu et.al[26] try to minimize transmission power based on the stateof the wireless channel. Mohapatra et. al.[27] identifiedseveral architectural techniques which can be coupled withOS level approaches to save CPU and memory energy whilestreaming video. In [28] the authors model the interde-pendence between different video packets to determine theoptimal retransmission scheme for HARQ processes but donot consider the energy implications.

There is other work, where video delivery is optimizedfor quality (e.g., [29] and citations therein). These effortsdo not explicitly consider energy due to video streaming.

VI. DiscussionAdaptive bit rate video: Current adaptive bit rate

streaming technologies such as Adobe and Silverlight (usedby Youtube and Netflix, respectively) are relatively simple.They work as follows. The server stores different bit rateversions of the same video. When the client requests avideo, the server sends the client a list of different bitrate videos it has in its storage. The client then makesdecisions on what bit rate video it wants to download on a

per chunk basis, where a chunk is simply a small portion ofthe video [30], [31], [32]. Chunk sizes (in terms of viewingtime) can range anywhere from 2 seconds to 10 seconds.The default bit rate is applied to the first chunk andusually corresponds to the maximum available bandwidth.Clients select the highest bit rate that is supported bytheir bandwidth [33]. This bandwidth is estimated bylooking at the time it takes to download a chunk andthe size of that chunk [33]. Current adaptive bit rateclients try to maximize bit rate given this single constrainton bandwidth. We argue that energy is a concern forclients and our model allows this factor to be taken intoperspective.

Our experiments show that transferring a high-resolution video over a channel with high PER results invery high energy consumption. Bandwidth aware clientscould still request this video because it can still be trans-mitted over the channel (bandwidth suffices). However, theuser may not want to spend that much energy. Further,while PER affects the bandwidth of the link, other factorssuch as load or how busy the enB is at that time, couldbe arguably more important in determining the bandwidthavailable for a session as shown in [34].

Our model applies to adaptive bit rate videos; thediscussion was focused on fixed bit rate videos for thepurposes of clarity. Some of the experiments computeenergy savings assuming a fixed bit rate video, again forthe purposes of making things clear. We point out thatour results, in particular Figs. 8 to 11, demonstrate theeffectiveness of our model in predicting power consumed atshort time scales. The same experiments also demonstratethe accuracy of our model across different bit rates. Recallthat two primary inputs to our model are PER and videobit rate. Power estimates can be made as often as necessarywhenever one of the inputs changes.

An energy aware adaptive bit-rate scheme would makesimilar chunk wise decisions as its non-energy aware coun-terpart. The client now has an added constraint, namelypower. The UE can estimate the power consumption withany bit rate video given the state of the channel usingour model. The client would now estimate (a) the high-est bit rate video given its bandwidth constraints (sayBitrateB and (b) the highest bit rate video given anenergy constraint (BitrateE). It would choose the bit ratethat satisfies both these constraints i.e., min(BitrateB ,BitrateE).

Impact of buffering: One can conceive a simplemodel which assumes that the entire video is bufferedat the server and then downloaded continuously. It isthen displayed to the user. In such a case, there will beno RRC state transitions (as discussed later, it will bein a continuous reception state). Now, one might arguethat one can calculate the time for which the interface isactive provides an indicator of the energy spent. One couldproportionally increase this time based on the perceivedpacket error rate.

In practice however, this model is insufficient becauseof two important characteristics. First, studies have shownthat major video streaming services (e.g., Youtube and

Netflix) do not buffer too much. The buffering is oftenlimited to a couple of seconds [30]. Since clients oftenterminate videos prematurely, the service providers areconcerned with wasted bandwidth and for this reasonnever buffer more than what is necessary. Netflix clientssometimes buffer up to 2 minutes because Netflix hostsvideos that are often hours long and some extra bufferingis supported in case clients want to skip ahead.

Because of the above reason, it is quite possible that asnetwork conditions change, the server side buffer becomestemporarily empty (and thus, the video is not continuouslydownloaded) which could trigger RRC state transitions.Our model is generic enough to take care of such cases;it is also applicable to the case where the server has asteady stream of video packets for a client for continuousdownloads.

Second, We also point out here that the video stream isnot necessarily transferred at the bit rate specified at theserver. If that was the case, one could simply use a linearmodel to determine how the energy would vary with bitrate. The video traffic is shaped not only because the servermay have varying loads (and thus transfers video at thespecified bit rate on average, but in a bursty manner), butalso because of retransmissions on the wireless channel dueto varying channel characteristics. Thus, it is inevitablethat the packets will experience some form of queuing atthe LTE server.

In summary, our model applies in cases where there isno buffering, any level of buffering or in the extreme casewhere a client buffers the entire video prior to playback.The video in question will still have to be transferredover an error prone wireless channel (which will resultin queuing and retransmissions). If a client is allowed tobuffer the entire video clip, all packets from the clip areinserted into the server buffer i.e., that buffer does notbecome empty until the entire video is transferred). Thissimply translates to very high values for λ1 and λ2. Ourmodel can be still applied and the power consumption fordifferent qualities can be calculated accordingly.

Motion in a video: Our framework accounts thetype of dynamics in a video stream (slow or fast motion).For ease of discussion, we have assumed that the videois homogeneous in this regard. However, a video maytransition between periods of fast motion to slow motionand vice versa. Tools such as AForge [7] can estimate theexpected durations of fast and slow motion in such cases;with these estimates (done over chunks of buffered video atthe server side), the expected energy savings can be easilycomputed with our framework.

Mobility If a UE is moving about a lot, this will causethe state of the channel to change (PER will change). Insuch cases, our model can simply recalculate power con-sumption with the new PER (can be potentially measuredas videos are downloaded). However when a UE moves outof the coverage area of an enB an LTE handover will benecessary. Our model does not capture this characteristic.

VII. ConclusionsIn this paper, we build an analytical framework to

capture the energy consumption due to video downloadsover LTE. Our framework takes as input, the type of video(fast vs slow motion and resolution), the link qualitiesexperienced (in terms of PER) and the power consumed ineach LTE state. It provides a quick and effective means ofdetermining the power consumption with various types ofvideos under different network conditions. We validate ourframework via extensive simulations and real experiments.

Acknowledgements: This work was supported bythe NSF NeTS grant 1320148. We thank our shepherdSamir Das for his constructive comments which helped ussignificantly improve the paper.

References[1] “Cisco Visual Networking Index: Forecast and Methodology,”

http://bit.ly/LVhmuL.[2] S. Sesia, I. Toufik, and M. Baker, LTE - The UMTS Long Term

Evolution: From Theory to Practice, 2nd ed. Wiley, Sep. 2011.[3] F. Q. J. Huang, A. Gerber, S. S. Z. M. Mao, and O. Spatscheck,

“A close examination of performance and power characteristicsof 4G LTE networks,” ACM MobiSys 2012.

[4] B. Vandalore, W. chi Feng, R. Jain, and S. Fahmy, “A surveyof application layer techniques for adaptive streaming of multi-media,” Real-Time Imaging, vol. 7, no. 3, pp. 221 – 235, 2001.

[5] A. Anand, A. Balachandran, A. Akella, V. Sekar, and S. Se-shan, “Enhancing video accessibility and availability usinginformation-bound references,” in ACM CoNEXT, 2013.

[6] S. Yi, S. K. Fayazbakhsh, Y. Guo, V. Sekar, Y. Jin, D. Kaafar,and S. Uhlig, “Trace-Driven Analysis of ICN Caching Algo-rithms on Video-on-Demand Workloads,” in ACM CoNEXT,December 2014.

[7] “AForge.NET,” http://www.aforgenet.com/framework/features/motion detection 2.0.html.

[8] J. Xue and C. W. Chen, “Mobile video perception: New insightsand adaptation strategies,” Selected Topics in Signal Process-ing, IEEE Journal of, vol. 8, no. 3, pp. 390–401, 2014.

[9] A. C. Bovik, The Essential Guide to Video Processing. Aca-demic Press, 2009.

[10] O. Ibe, “Markov Process for Stochastic Modelling,” AcademicPress, 2008.

[11] “LENA,” http://bit.ly/UEleow.[12] L. Zhang, B. Tiwana, Z. Qian, Z. Wang, R. P. Dick, Z. M.

Mao, and L. Yang, “Accurate online power estimation andautomatic battery behavior based power model generation forsmartphones,” in CODES/ISSS, 2010.

[13] “NS3,” http://www.nsnam.org/.[14] “EvalVid,” http://bit.ly/UOPQ6v.[15] “Monsoon Power Monitor,” http://bit.ly/1lh8JX1.[16] A. Schulman, V. Navda, R. Ramjee, N. Spring, P. Deshpande,

C. Grunewald, V. N. Padmanabhan, and K. Jain, “Bartendr:A practical approach to energy-aware cellular data scheduling,”in ACM Mobicom, 2010.

[17] N. Ding, D. Wagner, X. Chen, A. Pathak, Y. C. Hu, andA. Rice, “Characterizing and modeling the impact of wirelesssignal strength on smartphone battery drain,” in ACM SIG-METRICS, 2013.

[18] A. Jensen, M. Lauridsen, P. Mogensen, T. SÃÿrensen, andP. Jensen, “LTE UE power consumption model: For systemlevel energy and performance optimization,” in IEEE VTC,2012.

[19] C. Bontu and E. Illidge, “DRX mechanism for power saving inLTE,” IEEE Comm. Magazine, 2009.

[20] L. Zhou, H. Xu, H. Tian, Y. Gao, L. Du, and L. Chen, “Per-formance analysis of power saving mechanism with adjustableDRX cycles in 3GPP LTE,” in IEEE VTC 2008 (Fall), 2008.

[21] T. Kolding, J. Wigard, and L. Dalsgaard, “Balancing powersaving and single user experience with discontinuous receptionin LTE,” in IEEE ISWCS, 2008.

[22] J. Dohl and G. Fettweis, “Energy aware evaluation of ltehybrid-arq and modulation/coding schemes,” in Communica-tions (ICC), 2011 IEEE International Conference on, 2011, pp.1–5.

[23] M. Siekkinen, M. A. Hoque, J. K. Nurminen, and M. Aalto,“Streaming over 3G and LTE: How to save smartphone energyin radio access network-friendly way,” in Proceedings of the 5thWorkshop on Mobile Video, 2013.

[24] Y. Xiao, R. Kalyanaraman, and A. Yla-Jaaski, “Energy con-sumption of mobile YouTube: Quantitative measurement andanalysis,” in NGMAST, 2008.

[25] Z. He, Y. Liang, L. Chen, I. Ahmad, and D. Wu, “Power-rate-distortion analysis for wireless video communication under en-ergy constraints,” IEEE Transactions on Circuits and Systemsfor Video Technology, 2005.

[26] X. Lu, Y. Wang, and E. Erkip, “Power efficient h.263 videotransmission over wireless channels,” in International Confer-ence on Image Processing, 2002.

[27] S. Mohapatra, R. Cornea, N. Dutt, A. Nicolau, and N. Venkata-subramanian, “Integrated power management for video stream-ing to mobile handheld devices,” in ACM Multimedia, 2003.

[28] L. Badia and A. Guglielmi, “A markov analysis of automaticrepeat request for video traffic transmission,” in A World ofWireless, Mobile and Multimedia Networks (WoWMoM), 2014IEEE 15th International Symposium on, 2014.

[29] A. Pande, V. Ramamurthi, and P. Mohapatra, “Quality-oriented video delivery over lte using adaptive modulation andcoding,” in IEEE GLOBECOM, 2011.

[30] C. Muller, S. Lederer, and C. Timmerer, “An evaluation of dy-namic adaptive streaming over http in vehicular environments,”in Proceedings of the 4th Workshop on Mobile Video, ser. MoVid’12. ACM, 2012.

[31] S. Akhshabi, A. C. Begen, and C. Dovrolis, “An experimentalevaluation of rate-adaptation algorithms in adaptive streamingover http,” in Proceedings of the Second Annual ACM Confer-ence on Multimedia Systems, ser. MMSys ’11. ACM, 2011.

[32] T. Stockhammer, “Dynamic adaptive streaming over http –:Standards and design principles,” in Proceedings of the SecondAnnual ACM Conference on Multimedia Systems, ser. MMSys’11. ACM, 2011.

[33] J. Jiang, V. Sekar, and H. Zhang, “Improving fairness, effi-ciency, and stability in http-based adaptive video streamingwith festive,” in Proceedings of the 8th International Conferenceon Emerging Networking Experiments and Technologies, ser.CoNEXT ’12. ACM, 2012.

[34] A. Chakraborty, V. Navda, V. N. Padmanabhan, and R. Ram-jee, “Coordinating cellular background transfers using load-sense,” in Proceedings of the 19th Annual International Confer-ence on Mobile Computing & Networking, ser. MobiCom ’13.ACM, 2013.