Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks

London, 11th June 2015

Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks

IEEE ICC 2015 - SAC, Energy Efficient Wireless Systems

Lorenzo Carlà, Francesco Chiti, Romano Fantacci, A. Tassi a.tassi@{lancaster.ac.uk, bristol.ac.uk}

http://lancaster.ac.uk

http://bristol.ac.uk

Starting Point and Goals๏ Delivery of multimedia broadcast/multicast services over

4G/5G networks is a challenging task. Especially for the point of view of the user battery efficiency.

๏ During the reception of high data rate video streams, the user radio interface is in an active state for a not be negligible time. That has an impact on the battery, we minimize the active time.

๏ There are many studies dealing with DRX optimization but they mainly refer to Point-to-Point services.

Goals ๏ Advanced eMBMS Scenario - Scalable video service (such as

H.264/SVC) multicasting.

๏ Resource optimisation - Minimizing the transmission time of a video stream and, hence, the battery footprint.

2

Index

1. System Parameters and Performance Analysis

2. Proposed Resource Allocation Modeling and Heuristic Solution

3. Analytical Results

4. Concluding Remarks

3

1. System Parameters and Performance Analysis

System Model๏ One-hop wireless communication system composed of a Single

Frequency Network (SFN) and a multicast group of U users

5

๏ Layered service consists of a basic layer and multiple enhancement layers.

๏ All the BSs forming the SFN multicast the same layered video service, in a synchronous fashion.

๏ Reliability ensured via the RLNC principle.

BSBS

BSBS

M1/M2

(MCE / MBMS-GW)

SFN

41

23

UE3UEUUE 2

UE1UE4

LTE-A Core Network

6

๏ Encoding performed over each service layer independently from the others.

๏ The source node will linearly combine the data packets composing the -th layer and will generate a stream of coded packets , where

๏ is a layered source message of source packets, classified into service layersx = {x1, . . . , xK}

RLNC Principle

yj =klX

i=1

gj,i xi Coef%icients+of+the+linear+combination+are+selected+a+

certain+%inite+%ield

K1 K2 K3

x1 x2 xK. . .. . .

KL

KL`

{yj}j

7

RLNC and LTE-AData$stream

associated$with$$ $

⊗⊗ ⊗⊕

TB

MAC

PHY

Data$streamassociated$with$$ $

MAC$PDUassociatedwith

x1 x2 xK. . .

gj,1 gj,2 gj,K2

. . .xK2

y1 yj. . . . . .

Source$Message

. . .

yj

v1 v2

v2

Service+layers+arrive+at+the+MAC+

layer

Coded+elements+are+generated Depending+on+the+

MCS+a+certain+no.+of+cod.+el.+are+mapped+

onto+a+PDU+

๏ Coded elements of different layers cannot be mixed within a PDU ๏ One PDU per PHY layer Transport Block. TBs of the same layer are

transmitted with the same power.

N`(P`, t`) '$ru(P`)

t` tTTI

L`

%

Performance Model

8

๏ User collects coded elements associated with layerN`u `

N`(P`, t`) '$ru(P`)

t` tTTI

L`

%

Performance Model

8


Tx+pow.

N`(P`, t`) '$ru(P`)

t` tTTI

L`

%

Performance Model

8


Tx+pow. No.+of+PDU+tx

N`(P`, t`) '$ru(P`)

t` tTTI

L`

%

Performance Model

8



Cod.+el.+bit+length

TTI+durationUser+reception+rate

N`(P`, t`) '$ru(P`)

t` tTTI

L`

%

Performance Model

8


u `๏ A user recovers the layer if it collects linearly independent coded elements associated with that layer, which occurs with probability

✴ A. Tassi et al., “Resource-Allocation Frameworks for Network-Coded Layered Multimedia Multicast Services”, IEEE J. Sel. Areas Commun., vol. 33, no. 2, Feb. 2015

gu(P`, t`) =K`�1Y

j=0

1� 1

qN`(P`,t`)�j

�

K`


Cod.+el.+bit+length

TTI+durationUser+reception+rate

2. Proposed Resource Allocation Modelling and Heuristic Solution

Problem Formulation

10

๏ The battery efficiency is obtained by accommodating the transmission power and the number of PDU transmissions per service layer.

(MSP) minmax

`2{1,...,L}t` (1)

subject to

UX

u=1

�

⇣gu(P`, t`)� ˆ

�

⌘� ˆ✓`U ` 2 {1, . . . , L} (2)

K` t` dGoP

` 2 {1, . . . , L} (3)

LX

`=1

P` ˆP (4)

P` 2 R+, t` 2 N ` 2 {1, . . . , L} (5)

During+each+subframe+the+total+transmission+power+is+limited+

Each+service+level+shall+be+achieved+by+a+predetermined+fraction+of+users+

within+a+certain+time.+Max.+Sleep+Period

Problem Heurist ic๏ The MSP is an hard integer optimisation problem because of

the coupling constraints among variables. We proposed the following heuristic strategy.

11

(MSP) minmax

`2{1,...,L}t` (1)

subject to

UX

u=1

�

⇣gu(P`, t`)� ˆ

�

⌘� ˆ✓`U ` 2 {1, . . . , L} (2)

K` t` dGoP

` 2 {1, . . . , L} (3)

LX

`=1

P` ˆP (4)

P` 2 R+, t` 2 N ` 2 {1, . . . , L} (5)



11

(MSP) minmax

`2{1,...,L}t` (1)

subject to

UX

u=1

�

⇣gu(P`, t`)� ˆ

�

⌘� ˆ✓`U ` 2 {1, . . . , L} (2)

K` t` dGoP

` 2 {1, . . . , L} (3)

LX

`=1

P` ˆP (4)

P` 2 R+, t` 2 N ` 2 {1, . . . , L} (5)

(USP)Unconst.+Sleep+Period



12

(USP-`) min t` (1)

subject to

UX

u=1

�

⇣gu(P`, t`) � ˆ

�

⌘� ˆ✓`U (2)

K` t` dGoP

(3)

๏ Proposition - If the solution of (USP-l) exists, it belongs to

๏ However, the USP solution may not be feasible for MSP.

L`

.=

n

(P`

, t`

) 2 R+ ⇥ N�

�

�

K`

t`

dGoP

^P

U

u=1 �⇣

gu

(P`

, t`

)⌘

� �̂)= ✓̂`

Uo



12

(USP-`) min t` (1)

subject to

UX

u=1

�

⇣gu(P`, t`) � ˆ

�

⌘� ˆ✓`U (2)

K` t` dGoP

(3)

๏ Proposition - If the solution of (USP-l) exists, it belongs to

๏ However, the USP solution may not be feasible for MSP.

L`

.=

n

(P`

, t`

) 2 R+ ⇥ N�

�

�

K`

t`

dGoP

^P

U

u=1 �⇣

gu

(P`

, t`

)⌘

� �̂)= ✓̂`

Uo

t`

P`

Heurist ic Procedure

13

t`

P`

dGoP

๏ H-MSP makes feasible the optimum solution of USP.

Frontier+of USP-1USP-2

Frontier+of USP-3Frontier+of


13

t`

P`

dGoP


Optimum+values+of+++++++++++++++++,+++++++++++++++++and+

K1 K2 K3

USP-1 USP-2 USP-3




13

t`

P`

dGoP


(i) Start+from+the+UTP+solution+(ii) Alter+the+component+with+the+greatest+value+of++(iii) Repeat+the+proc.+until+the+power+budget+is+met.

t`

Optimum+values+of+++++++++++++++++,+++++++++++++++++and+

K1 K2 K3

USP-1 USP-2 USP-3



3. Numerical Results

Numerical Results

15

๏ We compared the proposed strategies with a classic Uniform Power Allocation (UPA) strategy

๏ System performance was evaluated in terms of

Relies+on+the+considered+LTELA+stack

(UPA) min

`2{1,...,L}t` (1)

subject to K` t` dGoP

` 2 {1, . . . , L} (2)

P` =ˆP/L ` 2 {1, . . . , L} (3)

✏=dGoP

� max

`=1,...,L(t`)

dGoP

Normalized+sleep+period

Numerical Results

16

SFN cell sector

Interfering cell sector

SFN base station

Interfering base station

Center of the Cell I

Center of the Cell II

Scenario+with+a+high+heterogeneity.+80+UEs+

equally+spaced

We+considered+ 3Llayer+and+4Llayer+

streams

Numerical Results

17

20 25 30 35 40 45 50 55 60 65 70 75 800

0.2

0.4

0.6

0.8

Power [W]

ε

MSPH-MSPUPA6 RBP9 RBP12 RBP

3Llayer+stream

MSP+and+HLMSP+are+close(PDUs+of+6+RBPs)

1+RBP+=+180+KHz

Numerical Results

17

20 25 30 35 40 45 50 55 60 65 70 75 800

0.2

0.4

0.6

0.8

Power [W]

ε


3Llayer+stream


UPA+cannot+always+%ind+a+solution (PDUs+of+6+RBPs)

1+RBP+=+180+KHz

Numerical Results

18

20 25 30 35 40 45 50 55 60 65 70 75 800

0.2

0.4

0.6

0.8

Power [W]

ε



UPA+cannot+always+%ind+a+solution (PDUs+of+6+RBPs)

1+RBP+=+180+KHz

4Llayer+stream

4. Concluding Remarks

Concluding Remarks

20

๏ We propose an optimal and heuristic radio resource allocation strategy, namely MSP and H-MSP strategies, which maximize the user sleep period and improve the reliability of communications by means of an optimized RLNC approach

๏ Not only the the user energy consumption is reduced but also the developed strategies can meet the desired QoS levels

๏ Results show that the developed H-MSP strategy provide a good quality feasible solution to the MSP model in a finite number of steps

๏ The proposed strategy is characterized by sleep periods that are up to 40% greater than those provided by the considered UPA approach.

Thank you for your attention

For more informationhttp://goo.gl/Z4Y9YF

A. Tassi, I. Chatzigeorgiou, and D. Vukobratović, “Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast

Services”, IEEE J. Sel. Areas Commun., vol. 33, no. 2, Feb. 2015

http://goo.gl/Z4Y9YF

London, 11th June 2015

Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks

IEEE ICC 2015 - SAC, Energy Efficient Wireless Systems

Lorenzo Carlà, Francesco Chiti, Romano Fantacci, A. Tassi a.tassi@{lancaster.ac.uk, bristol.ac.uk}

http://lancaster.ac.uk

http://bristol.ac.uk

Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks

Internet

Transcript of Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS Networks