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Streaming To Mobile Users In A Peer-to-Peer Network

Jeonghun NohStanford University

350 Serra Mall

Stanford, CA, [email protected]

Mina MakarStanford University

350 Serra Mall


Bernd GirodStanford University

350 Serra Mall


ABSTRACT

Streaming video to mobile users is rapidly emerging as acrucial multimedia service. One of the stumbling blocks inmobile streaming is the heterogeneity found in mobile de-vices: diverse display size, computing power, memory, andmedia capabilities. Given this heterogeneity, video transcod-ing is often required to satisfy the requirements of differentmobile users. In this paper, we propose a peer-to-peer (P2P)

method for mobile streaming. In the proposed method,peers other than mobile users, calledfixed nodes, contributetheir computing power for transcoding. We further pro-pose the interleaved distributed transcoding (IDT) schemethat allows multiple fixed nodes to perform transcoding fora mobile device. Distributed transcoding not only lowerscomputation at fixed nodes, but also achieves error resilienceagainst packet loss. The IDT scheme conforms to the H.264/AVC baseline profile, which requires no modification to de-coders found in many mobile phones. We analyzed the effectof distributed transcoding under peer churn. Extensive sim-ulations show that mobile streaming based on the proposedscheme is robust to packet loss due to peer churn and adversewireless channel conditions.

Categories and Subject DescriptorsC.2 [Computer-communication networks]: Distributedsystems

Keywords

Peer-to-peer network, video streaming, transcoding, mobiledevice, heterogeneity

1. INTRODUCTIONLive streaming to mobile devices, such as cellular phones

and Personal Digital Assistants (PDAs), is a challengingtask especially due to their heterogeneity: they differ in dis-play size, main memory, processor, media capability, and

network access technology. In typical streaming systems,

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live media adaptation is performed to meet the requirementsof heterogeneous mobile users. For video, media adaptationis often achieved byvideo transcoding [13]. Video transcod-ing converts an original video bitstream to a new bitstreamfor a different encoding standard, smaller spatial resolution,reduced frame rate, or reduced quality (due to coarser quan-tization). However, transcoding poses a considerable com-putational burden on the streaming server because mobiledevices often require individually customized transcoding.

In this paper, we propose a peer-to-peer (P2P) stream-ing method for mobile devices [4]. P2P streaming is a po-tentially cost-effective alternative to server-based streaming.In P2P systems, users not only consume media contents,but also contribute their uplink bandwidth or local storage.Thus the system can scale well as users bring resources inthe system. In this study, we refer to personal computersconnected to the network over a wired connection as fixednodes, as opposed to mobile nodes. By harnessing the pro-cessing power of the fixed nodes, the transcoding burdenof the servers can be reduced or eliminated. On the otherhand, due to their limited resources (battery, uplink speed),mobile nodes in the proposed P2P system are treated asleeches, i.e., peers that only receive packets but do not relay

the packets to other peers.P2P-based streaming systems often suffer frompeer churn:when peers leave the system without prior notice, otherpeers who are connected to the departing peers may ex-perience temporary video disruption and/or disconnectionfrom the system. To address this problem, we propose adistributed transcoding scheme that allows fixed nodes toperform transcoding for a mobile device. After a mobiledevice connects to multiple fixed nodes as its parents, eachparent generates a substream by transcoding the originalvideo. These substreams are transmitted and then assem-bled at the mobile device as if they were a single stream.If the mobile device loses some of its parents, it still re-ceives substreams from the other parents and decodes theincoming video partially with graceful degradation. In addi-

tion, the proposed transcoding scheme distributes transcod-ing overhead to multiple fixed nodes. We implement theproposed distributed transcoding scheme in a way that con-forms to the H.264/AVC baseline profile. This allows anyH.264/AVC video decoders to decode the video produced bythe proposed scheme.

The remainder of the paper is structured as follows. Sec-tion 2 presents the work related to streaming video to mo-bile devices. Section 3 describes the proposed distributedtranscoding and its implementation. In Section 4, we ana-


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1 2 3 4 DecodingIntermediate

processingEncoding 1 3

Figure 1: A cascaded transcoder. An original video stream flows into the decoding unit. The intermediateprocessing unit transforms the decoded stream by performing frame rate conversion, downsampling, cropping,or any other preprocessing before encoding. The output of the encoder is the transcoded bitstream.

lyze the effect of distributed transcoding against peer churn.In Section 5, we provide simulation results.

2. RELATED WORKSafak et al. propose a server-based adaptive video transcod-

ing system for mobile users in [2]. A video proxy, locatedat the edge of two or more networks, adaptively transcodesvideo considering the network conditions and constraints ofmobile users in the GPRS network. Warabino et al. also

propose a server-based video transcoding scheme for mobileusers in [1]. The client proxy collects mobile client profilesand request transcoding to the video proxy. Transcodedvideos are then sent back to the client proxy, and the clientproxy relays them to each client. Although server-based sys-tems [1,2] are more reliable than P2P-based systems, serverscan easily become a bottleneck as the number of mobile usersincreases.

In [5], several multicasting schemes for streaming to mo-bile users are surveyed. The authors compare solutions thatextend IP multicast to support mobile users. Since IP mul-ticast trees are constructed at the network layer, these solu-tions often focus on the reduction of the overhead associatedwith the reconfiguration of the trees due to the mobility ofmobile users. The authors then suggest a combination ofapplication layer and network layer multicast solutions.

P2P-based video-on-demand (VoD) mobile streaming wasinvestigated by the authors in [6]. In the system they pro-posed, the mobile user establishes a one-to-one connectionto a proxy peer chosen in the P2P network. Through thatproxy peer, the mobile user searches for a pre-encoded videostored somewhere in the P2P network and downloads it.However, there is no discussion in [6] on the needs for videotranscoding and error resilience in case of proxy peer failure.

The contribution [7] also studied P2P-based VoD stream-ing. When a video is downloaded from several source peers,media transcoding is performed by multiple peers to meetthe requirements of the destination peer. To combat peerchurn, the video quality is adapted by the transcoders based

on the feedback from the destination peer. However, no dis-cussion is provided regarding the way video transcoding isperformed at multiple peers and how distributed transcod-ing and peer churn affects the video quality quantitatively.

In [8], a fully collaborative P2P streaming system is pro-posed. Instead of all users, only a few mobile users pulla video from the video server through base stations. Thepulled video is then shared with other neighboring users viaa free broadcast channel, such as Wi-Fi or Bluetooth. Theproposed system is shown to be scalable with the numberof users and is shown to reduce cellular bandwidth usage.

However, it works well only when a sufficient number ofusers watching the same video belong to the same base sta-tion, and the neighboring users are physically close to eachother to lessen power consumption incurred while the videois being relayed among peers.

3. VIDEO TRANSCODINGIn this section, we describe how video transcoding is per-

formed at one or multiple locations for a mobile node in our

P2P system. We consider a P2P system that consists of fixednodes and mobile nodes; fixed nodes are peers that receiveand consume the original video emanating from the videosource. Mobile nodes are peers that cannot receive the orig-inal video due to limited downlink bandwidth, or/and can-not consume the original video due to limited video decodingcapabilities. Thus, it is desirable to perform video transcod-ing to adapt the original video to mobile nodes. Fixed nodesperform transcoding to adapt the original video accordingto the individual requirements of each mobile node. In thisstudy, we adopt the cascaded transcoding scheme shownin Fig. 1

3.1 Interleaved distributed transcodingThe multiple parent approach is a popular solution to pro-

vide robustness to peer churn [911]. In this approach, apeer has multiple peers as parents. When a parent disap-pears, only a subset of video packets are lost, which allowsfor graceful video degradation. We propose a distributedtranscodingscheme that allows multiple fixed nodes to per-form transcoding for a mobile device. A mobile node se-lects multiple fixed nodes as parents and the parents per-form transcoding collaboratively. A straightforward schemewould be that each parent transcodes the entire originalvideo and delivers a disjoint substream of it to a mobilenode. When one of its parents disconnects, the mobile nodeasks for retransmissions of the missing substream from otherparents. However, such a straightforward scheme needlesslywastes computing power at the parents.

To reduce processing redundancy, yet achieve robustnesswith multiple parents, we propose interleaved distributedtranscoding (IDT), which is illustrated in Fig. 2. In theillustration,K= 4 parents are generating transcoded sub-streams with a Group of Pictures (GOP) of 12 frames. Theoriginal GOP size is assumed to be larger than 14 in Fig. 2.This illustration demonstrates that the GOP size of thetranscoded stream can be selected independently of the oneof the original stream. In transcoding, the original videoframes are first decoded. In this illustration, the decodedbitstream is downsampled to smaller frames in the spatial


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the effective frame rate becomes (K1K

)f(no packet loss overthe wireless channel is assumed for this discussion). For in-stance, when K= 2, if the mobile node is in State 1, thenthe effective frame rate is 0.5f. WhenK = 4, then if themobile node is in State 3, the effective frame rate is 0.75f.

Table 1 shows the average fraction of time during which aparticular number of parents (substreams) of a mobile nodeare missing under different peer churn rates. /, denotedby , indicates the ratio of the parent average lifetime to

the parent average recovery time. As increases, the stateprobability of StateK(no parents are missing) also increasesand the state probability of all the other states decreases.It is obvious because the mobile node is more likely to haveall parents alive when the recovery time is shorter or theaverage lifetime is longer.

In Table 2, the effect of peer churn on the number ofmissing parents is shown with the total number of parentsKvaried from 1 to 4. As a mobile node increases the totalnumber of parents it connects to, the probability that allparents are missing approaches 0, whereas the probabilitythat at least one parent is missing increases (whenis large,the probability of missing one parent (StateK1) increaseslinearly asKincreases). We also found that regardless ofK,

the average fraction of missing frames is

1

+1 (proof is omit-ted due to space constraint). This indicates that althoughconnecting to multiple parents does not reduce the averagenumber of missing packets, it effectively alleviates the de-gree of video degradation (reduced effective frame rate) atthe expense of more frequent video degradation.

Table 1: The average fraction of time during whicha particular number of parents of a mobile node aremissing. The total number of parents is set to 4./ is the ratio of the parent average lifetime to theaverage recovery time.

/ Number of missing parents

0 1 2 3 4

10 0.683 0.273 0.041 0.003 030 0.877 0.117 0.006 0 050 0.924 0.074 0.002 0 0

Table 2: The average fraction of time during whicha particular number of parents are missing given dif-ferent total number of parents ( is set to 30.)

Total number Number of missing parentsof parents 0 1 2 3 4

1 0.968 0.032 N/A N/A N/A2 0.936 0.062 0.002 N/A N/A3 0.906 0.091 0.003 0 N/A

4 0.877 0.117 0.006 0 0

5. EXPERIMENTAL RESULTSWe conducted computer simulations to evaluate the pro-

posed distributed transcoding scheme in a P2P environment.We implemented the IDT algorithm by modifying the x264encoder [13]. The x264 encoder is an open source libraryfor encoding videos in the H.264/AVC syntax. The Fore-mansequence and the Mother & daughtersequence in CIFresolution were used as the original videos, coded at 590

kbps and 223 kbps, respectively, using the H.264 main pro-file. The GOP size was 15 frames, and two B frames weregenerated between anchor frames. The frame rate of theoriginal video was 30 fps. This original video was streamedlive to a population of peers from the video source. Peersincrementally constructed the overlay as they joined the sys-tem [11] . When a fixed node was connected to the system,it received the original video as long as its downlink capacitywas higher than the video bitrate. For simplicity, we assume

that all fixed nodes had downlink capacity larger than thevideo bitrate.

When a mobile user joins the system, it searches for Kfixed nodes that have available uplink bandwidth and pro-cessing power. We assumed that the number of fixed nodesexceedsK. After the mobile user findsKfixed nodes as par-ents, it assigns them unique Parent IDs (from 1 toK). Then,it requests them to trancode disjoint sets of video frames(substreams). For the synchronization of substreams, par-ents add meta data to substreams, such as the time stamp ofa GOP. During the parent-coordination process, the mobilenode examines its device-specific profile, such as the mediadecoding capability, display size, and users preference. Italso detects time-varying parameters including the remain-

ing battery capacity and the maximum downlink bandwidthof the wireless channel. Based on the collected information,the mobile node determines the video quality (e.g., quantiza-tion parameter), frame rate, and spatial resolution. In oursimulations, we kept the frame rate of the original video.Spatial resolution was reduced from CIF to QCIF. For atranscoded video, the GOP size was set to 24. The quan-tization parameter and the number of parents were variedacross different simulations.

The fixed nodes lifetime is exponentially distributed withan average of 90 seconds. When a fixed node serving as aparent leaves the system, its child node finds a different fixednode to recover the missing substream. The recovery timeis exponentially distributed with an average of 3 seconds.Although the IDT provides error resilience on its own, it is

sensitive to I frame loss. When Parent 1 failure is detected,the mobile node selects one of its available parents as the newParent 1. When I frames are lost due to the lossy channel,retransmission is requested for the missing I frames. Toavoid self-congestion, retransmissions of P frames are notrequested.

Figs. 5 and 6 show simulation results for single-parenttrans-coding and the interleaved distributed transcoding (2and 4 parents). A distortion measured in PSNR is computedbetween the input stream to the IDT encoders (the bistreamafter the intermediate processing unit) and the assembledstream. TheForemansequence contains much more motionthan the Mother & daughter sequence and requires morebits to encode, and concealment of lost frames is more diffi-cult. The solid curves show the rate-distortion performancewithout packet loss and without peer churn. The single-parent transcoding shows the highest coding efficiency inthis scenario. When the number of parents increases, thedistance between frames in a substream increases and thislowers inter-frame temporal correlation.

Now we consider the lossy scenario, where video degra-dation is incurred by two different sources: packet loss overthe wireless channel and peer churn. The wireless channelis modeled by the Gilbert-Elliot model, as a discrete-timeMarkov chain with two states. Table 3 shows the Gilbert


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100 200 300 400 50030

31

32

33

34

35

36

37

38

bitrate [kbps]

P

SNR[

dB]

IDT: 2 parents, no packet lossIDT: 2 parents, lossy scenario

IDT: 4 parents, no packet loss

IDT: 4 parents, lossy scenario

MDC: 2 parents, no packet loss

MDC: 2 parents, lossy scenario



Figure 7: Foremansequence: Comparison of IDT andMDC.

0 50 100 15033

34

35

36

37

38

39

40

bitrate [kbps]

P

SNR[

dB]

IDT: 2 parents, no packet lossIDT: 2 parents, lossy scenario

IDT: 4 parents, no packet loss

IDT: 4 parents, lossy scenario





Figure 8: Mother & Daughtersequence: Comparisonof IDT and MDC.

transcoding. The proposed interleaved distributed transcod-ing (IDT) is shown to lower the computation that each par-ent has to perform. The IDT scheme also provides error

resilience against packet loss due to peer churn and to ad-verse wireless channel conditions. By analyzing the effectsof peer churn, we showed that distributed transcoding bal-ances the degree of video degradation with the occurrence ofvideo degradation. Simulation results show that IDT out-performs multiple description coding (MDC) by 1 to 1.5 dB.We also implemented a real-time IDT encoder and verifiedthe results of our analysis and simulations.

7. ACKNOWLEDGMENTS

Several schemes for distributed video transcoding for mo-bile devices were explored by Dr. Frank Hartung while hevisited our group during the spring of 2008 on leave from

Ericsson Eurolab. Support of this work through a gift fromDeutsche Telekom Laboratories is gratefully acknowledged.

8. REFERENCES

[1] A. Warabino, S. Ota, D. Morikawa, and M. Ohashi,Video transcoding proxy for 3G wireless mobileinternet access,Communications Magazine, pp.6671, Jan. 2000.

[2] S. Dogan, A. Cellatoglu, M. Uyguroglu, A. Sadka, andA. Kondoz, Error-resilient video transcoding forrobust internetworkcommunications using GPRS,IEEE Transactions on Circuits and systems for videotechnology, vol. 12, no. 6, pp. 453464, 2002.

[3] A. Vetro, C. Christopoulos, and H. Sun, Videotranscoding architectures and techniques: anoverview,IEEE Signal Processing Magazine, vol. 20,no. 2, pp. 1829, 2003.

[4] J. Noh, P. Baccichet, F. Hartung, A. Mavlankar, andB. Girod, Stanford Peer-to-Peer Multicast (SPPM) overview and recent extensions, Proc. of InternationalPicture Coding Symposium (PCS), Chicago, Illinois,USA, invited paper,, May 2009.

[5] A. Dutta, J. Chennikara, W. Chen, O. Altintas, andH. Schulzrinne, Multicasting streaming media to

mobile users,IEEE Communications Magazine,vol. 41, no. 10, pp. 8189, 2003.

[6] S. Venot and L. Yan, On-demand mobile peer-to-peer

streaming over the JXTA overlay,Mobile UbiquitousComputing, Systems, Services and Technologies,(UBICOMM), pp. 131136, Nov. 2007.

[7] F. Chen, T. Repantis, and V. Kalogeraki,Coordinated media streaming and transcoding inpeer-to-peer systems, in19th IEEE InternationalParallel and Distributed Processing Symposium, 2005.Proceedings, 2005.

[8] M. Leung and S. Chan, Broadcast-based peer-to-peercollaborative video streaming among mobiles,IEEETransactions on Broadcasting, vol. 53, pp. 350361,2007.

[9] M. Castro, P. Druschel, A.-M. Kermarrec, A. Nandi,A. Rowstron, and A. Singh, SplitStream:

High-bandwidth content distribution in a cooperativeenvironment,Proc. of IPTPS03, Berkeley, CA, pp.298 313, Feb. 2003.

[10] V. N. Padmanabhan, H. J. Wang, P. A. Chou, andK. Sripanidkulchai, Distributing streaming mediacontent using cooperative networking,Proc. of ACMNOSSDAV, Miami Beach, FL, pp. 177 186, May2002.

[11] E. Setton, J. Noh, and B. Girod, Rate-DistortionOptimized Video Peer-to-Peer Multicast Streaming,Workshop on Advances in Peer-to-Peer MultimediaStreaming at ACM Multimedia, Singapore, pp. 3948,Nov. 2005.

[12] L. Kleinrock, Queueing systems: Volume 1 theory,

John Wiley & Sons, Inc., 1975.[13] x264,http://www.videolan.org/developers/x264.html.

[14] A. Konrad, B. Zhao, A. Joseph, and R. Ludwig, AMarkov-based channel model algorithm for wirelessnetworks,Wireless Networks, vol. 9, no. 3, pp.189199, 2003.

[15] J. Apostolopoulos, T. Wong, W. Tan, and S. Wee,On multiple description streaming with contentdelivery networks, in IEEE INFOCOM 2002.Proceedings, vol. 3, 2002.

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