Opti l All i fimal Resource Allocation for Video Streaming...

i l All i fOptimal Resource Allocation for Video Streaming over DistributedVideo Streaming over Distributed Communication Networks

Ling GuanRyerson Multimedia Research Laboratory &Ryerson Multimedia Research Laboratory &Centre for Interactive Multimedia Information MiningDepartment of Electrical and Computer Engineering,R U i it T t C d

12/7/2009 1

Ryerson University, Toronto, [email protected], http://www.rml.ryerson.ca/

Acknowledgmentg

The presenter would like to thank Dr. Yifeng He p gfor his persistent effort in making this research a true success

The presenter also would like to thank Dr. Ivan Lee for his continuous contributions to this workLee for his continuous contributions to this work

This research is supported by The Canada Research Chair (CRC) Program The Canada Research Chair (CRC) Program,

Canada Foundation for Innovations (CFI), Th O t i I ti T t (OIT) d The Ontario Innovation Trust (OIT), and

Ryerson University

12/7/2009 2

Major Publicationsj Y. He, I. Lee and L. Guan, “"Distributed throughput optimization in

P2P VoD systems ” to appear in IEEE Transactions on MultimediaP2P VoD systems, to appear in IEEE Transactions on Multimedia. Y. He, I. Lee and L. Guan, “Distributed algorithms for network

lifetime maximization in wireless visual sensor networks,” IEEE Transactions on Circuits and Systems for Video TechnologyTransactions on Circuits and Systems for Video Technology(subject to minor revision).

Y. He, I. Lee and L. Guan, “Optimized video multicast in wireless ad hoc network using network coding ” to appear in IEEE Transactionshoc network using network coding, to appear in IEEE Transactions on Circuits and Systems for Video Technology.

Y. He, G. Shen, Y. Xiong and L. Guan, “Optimal prefetching scheme in P2P VoD applications with guided seeks ” accepted byscheme in P2P VoD applications with guided seeks, accepted by IEEE Transactions on Multimedia

Y. He and L. Guan, “Optimal resource allocation in distributed visual communications ” in Intelligent Multimedia Communication:visual communications, in Intelligent Multimedia Communication: Techniques and Applications, C.W. Chen, Z. Li and S. Lian, eds, Springer-Verlag, to be published in 2009.

12/7/2009 3

Outline

Motivation and contributions Principles of convex optimization Resource allocation in distributed video Resource allocation in distributed video

communication systemsThro ghp t ma imi ation in P2P VoD applications Throughput maximization in P2P VoD applications

Network lifetime maximization in wireless visual sensor networksnetworks

Optimization for video streaming over wireless ad hoc networksnetworks

Conclusions

12/7/2009 4

Motivation

Many multimedia applications involve real-a y u ed a app ca o s o e eatime video transmissions over distributed networks Some examples:networks. Some examples:

P2P VoD applications Video streaming over wireless ad hoc networks Video streaming over wireless ad hoc networks Wireless visual sensor networks

Distributed algorithms to optimize resource Distributed algorithms to optimize resource allocations in distributed networks

Each node has local knowledge Each node has local knowledge No centralized controller Scalability

12/7/2009 5

Scalability

Challengesg P2P VoD applicationspp

Limited bandwidth Unreliable and dynamic peers Random seeks

Video streaming over wireless ad hoc networks High transmission error rate Source rate allocation Optimized routing scheme Optimized routing scheme

Wireless visual sensor networks Video compression consuming a large amount of power Video compression, consuming a large amount of power Trade-off between network life time and video quality

12/7/2009 6

Contributions

Formulate resource allocation problems in o u a e esou ce a oca o p ob e sdistributed networks based on convex optimization andoptimization, and

Solve them using distributed algorithmsTh h t i i ti i P2P V D li ti Throughput maximization in P2P VoD applications

Optimized video streaming over wireless ad hoc t knetworks

Network lifetime maximization in wireless visual t ksensor networks

12/7/2009 7

Outline





Conclusions

12/7/2009 8

Convex optimization problemp p

Convexity is often viewed as the “watershed” between easy and hard y yoptimization problems.

Convex optimization: the primal problem [Boyd2004]

miww

i ,...,1 ,0 :Subject to :minimize 0

xx

piqi ,...,1 ,0 x

functions,convex are , e,on variabloptimizati is where 0 xxRx in ww

function. affine is xiq

Solution:Distributed algorithm: Lagrange duality properties construct a dual problem

dual decomposition solve dual problem with subgradient method obtain primal optimization variable from dual variables

12/7/2009 9

Convex function and affine function

Convex function:

xqWh t i ffi f ti ? xiqWhat is affine function?

Definition: 0 and ,0Let 21 xqxq ii [Boyd2004]

,0haveweif

,1 21

yqxxy

i R

affinealwaysisfunctionLinearfunction. affine is Then ,

xqyq

i

i

12/7/2009 10

affine. alwaysisfunction Linear

Dual solutionConstruct a dual problem [Boyd2004]

The Lagrangian:

,, 0 xxxvλx p

ii

m

ii qvwwL

. variablesdual are ,wher 11

vλ

ii

The dual function:

p

ii

m

ii qvwwLg 0minmin ,,, xxxvλxvλ The dual function:

iii

iii qvwwLg

110minmin ,,, xxxvλxvλ

XX

The dual function is always concave

The minimum of several linear functions is always concave

The dual problem: 0:Subject to, :maximize

λvλg

The dual function is always concave.

D l bj ti f ti0 :Subject to λ Dual objective function

xxxxxxxvλX

011

011

0min, wqvwwqvwwgp

iii

m

iii

p

iii

m

iii

12/7/2009 11

Dual objective value is no larger than primal objective value.

Dual solutionDuality gap: 0 -*

0 *gwd** gg(u,v)g value,objective dual maximal theis

*0

*0 ) w( value,objective primal minimal theis ww x

Strong duality: under Slater's condition,*gwd *

0 :is that ,0gapduality

00

g0,g pySlater’s condition: There exists a x that satisfies:

miw 10 x piq 10xand miwi ,...,1 ,0 x piqi ,...,1 ,0 xand

In other word, there exist a strictly feasible point

Weak duality: d > 0

12/7/2009 12

Optimalityp y

Objective value of primal problem

Objective value of dual problem Duality gap=0,Obtain minimal primal objective and maximal dual objective at the

Optimal primal variables

same time

12/7/2009 13

Optimal dual variables

Subgradientg• A subgradient of function f at point x is any vector g, g p y g

that satisfies the inequality: f(z)>=f(x)+gT(z-x) for all z, f is a convex function

[B d2004]

12/7/2009 14

[Boyd2004]

SubgradientgSubgradient method to update dual variables [Bertsekas2003]

p,...ifvv

m,...i hk

ikk

ik

i

ki

kki

ki

,1

,1 ,0max1

1

p,fiii , lyrespective ,at oft subgradien theis , where k

ik

ik

ik

i v-gfh miwh kkk 1* vλx p,...iqf

m,...iwhkk

ik

i

ii

,1

,1 *

v,λx

v,λx

Non-summable diminishing Step size for convergence:Non summable diminishing Step size for convergence:

1

,0lim ,0k

kk

k

k

•Find optimal primal variables from dual variables: vλ,xvλ,x , inf arg* L

12/7/2009 15

Example: convex optimizationp p 04S bj

2056.0 :minimize2

20 xxxw

5]. 0[ ,04 :Subject to 2

1

xxxxw

16

18

20

16

18

20

10

12

14

l obj

ectiv

e va

lue

10

12

14

obje

ctiv

e va

lue

11.13 11.13

2

4

6

8

Prim

al

2

4

6

8

Dua

l

Optimal primal variable=2.56Optimal dual variable=0.6

0 0.5 1 1.5 2 2.50

Primal variable0 0.5 1 1.5 2 2.5 3

0

Dual variable

Primal Objective Value Dual Objective Value

12/7/2009 16

Primal Objective Value Dual Objective Value

Regularization term makes dual function gdifferentiable

Non-differentiable Differentiable

.-xx

01 :Subject to2 :minimize

2

2.5=0=0.05=0.1=0.15

Non differentiable Differentiable

jLagrange dual function:

-xxg 1 2 )( 1.5

2

l fun

ctio

n va

lue

2i i i 2

1

Lagr

ange

dua

l

.-xxx01 :Subject to

2 :minimize 2

Lagrange dual function:0 0.5 1 1.5 2 2.5 3 3.5 4

0

0.5

Lagrange dual function:

-xxg 1x 2 )( 2

12/7/2009 17

Dual decompositionpWhy solve the primal problem by first solving the dual problem?

1. Dual problem is always convex, can be efficiently solved.2. Dual problem can be decomposed easily, leading to distributed algorithm

D l d iti ( t bt i di t ib t d l ith )Dual decomposition: ( to obtain distributed algorithm)

Primal problem:

xfxf :minimize

D l bl

cx xxfxf

21

21

:Subject to :minimize

Dual problem:

infinf

inf :maximize 2121

cxxfxxfcxxxfxf

Dual variables update:

0 :Subject to

infinf 2211

λcxxfxxf

211 ,0max xxckkk

12/7/2009 18

Primal decompositionp

Primal problem:p

cx x

xfxf

21

2211


*2

*1 :max ffimize

α

Master problem

c x

xf

2

22


1

11


xxf

Subproblem 1 Subproblem 2

12/7/2009 19

Distributed solution

Original optimization problemProblems for FormulationOriginal optimization problem

(primal problem)video streaming over distributed networks

Dual problemUpdate dual variables

Subproblem 1 Subproblem N

Compute primal variable x1at node 1

Compute primal variable xNat node N

12/7/2009 20

Outline





Conclusions

12/7/2009 21

Outline





Conclusions

12/7/2009 22

Client/server VoD vs. P2P VoD/ Client/server VoD Server

Server bandwidth bottleneck

P2P li ti P2P applications P2P VoD, P2P live TV

Peer

Client/server

Single-layer coded P2P VoD Each user receives the same quality

Source node

q y

Scalable P2P VoD Lower bandwidth lower quality High bandwidth higher quality

Peer

12/7/2009 23

P2P

Related work P2P VoD applications

Existing P2P architectures Existing P2P architectures Buffer-forwarding architectures:

Tree-based: P2VoD [Do2004]Mesh based [Li2006] Mesh-based [Li2006]

Storage-forwarding architecture: VMesh [Yiu2007] Hybrid-forwarding architecture [[YHe-ICASSP08,YHe-TMM08a]

E i i i i i i P2P Existing optimizations in P2P Minimum-Delay for constant-bit-rate (CBR) P2P media session

[Wu2005] Distributed auction algorithm for rate allocation [Li2006] Maximization of throughput in scalable P2P VoD systems taking

into account packet loss due to excessive delay at each link [YHe-CICME07a]

Prefetching in P2P [YHe-TMM08b]

12/7/2009 24

Scalable VoD: prioritized coding schemep g

Prioritized coding scheme g Originally proposed in [Chou2003], useful for video

broadcast Layered coding + prioritized packetization + network

coding (at source and intermediate nodes) Advantages:

Scalable Resilient to packet loss Duplicate-free

A l th h t t i l d t hi h lit A larger throughput at a receiver leads to a higher quality

12/7/2009 25

Buffer-forwarding overlay for P2P VoDg y

Existing approaches to construct Existing approaches to construct buffer-forwarding overlay Tree-based Mesh-based

Our work is on the module of throughput maximizationthroughput maximization

Peer i

Module of buffer-forwarding overlay construction

Module of throughput maximization

Allocate rate at each outgoing link

Peer i

overlay construction maximization each outgoing link

12/7/2009 26

Graph modelp A network can be modeled as a directed graph

G=(N,L) N is the set of nodes

L i th t f li k L is the set of links Matrix to represent the node-link relationship

00011A: the relationship between the node and its connected links

A+ th l ti hi b t th

11 2

110001011001101000

A

00011

Node 2

A+ : the relationship between the node and its outgoing links

A th l ti hi b t th

23

4 5

3

00000100000110000011

A

A- : the relationship between the node and its incoming links

A= A+ A-

45

11000001100000100000

AA network

12/7/2009 27

A= A+ - A

Graph modelp

Network graphNetwork graphA P2P is modeled by a directed graph G = (N, L)

otherwise.,0, node intolink incomingan is link if,1, node fromlink outgoingan is link if,1

ilil

ailMatrix A:

.otherwise,0, node fromlink outgoingan is link if,1 il

ailMatrix A+:

.otherwise,0, node intolink incomingan is link if,1 il

ailMatrix A-:

12/7/2009 28

Throughput maximization in buffer-

P bl f l ti

g pforwarding systemsProblem formulation:

1 maximize pxa l

llil Aggregate throughputAggregate throughput Ll

lNi Ll

llil xpxa 21 minimize

, subject to

NiI

Ni, sxa rLl

lil

Ni Ll

Source rate constraint

D l d b d idth t i t

gg g g p LlNi Ll

, ,

,

NiOxa

Ni, Ixa

ilil

iLl

lil

Download bandwidth constraint

Upload bandwidth constraint

0

, ,

Ll

Llxcx lLm

mlml

Ll

Link-forwarding constraint

. ,0 Llxl

Original optimization problem:Linear Programming (LP) problem

Converted to:Strictly convex optimization problem distributed algorithm to solve it

12/7/2009 29

Linear Programming (LP) problemStrictly convex optimization problem, distributed algorithm to solve it

Throughput maximization in buffer-g pforwarding systems (cont.)Simulation results:Simulation results:

0.5

Centralized LPDistributed optimizationProportional allocation0.5

Centralized LPDistributed optimizationProportional allocation

Achievable throughput in buffer-forwarding architecture

0.4

Proportional allocationEqual allocation

Mbp

s] 0.4

Proportional allocationEqual allocation

Mbp

s]

Any better architecture to improve the achievable th h t?

02

0.3

thro

ughp

ut [M

02

0.3

thro

ughp

ut [M throughput?

0.1

0.2

Ave

rage

t

0.1

0.2

Ave

rage

t

C i f th h t

100 200 300 400 5000

Number of peers

100 200 300 400 5000

Number of peers

12/7/2009 30

Comparison of average throughput

Impact of regularization factorp g

3000

3500

4000

erge

nce

0.4

0.405

0.41

ps]

2000

2500

3000

ratio

ns fo

r con

ve

0.39

0.395

0.4

thro

ughp

ut [M

bp

500

1000

1500

Num

ber o

f ite

r

0.375

0.38

0.385

Ave

rage

t

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

Regulation factor0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0.37

Regulation factor

C l i Sub optimalityComplexity Sub-optimality

Throughput maximization in buffer-forwarding P2P VoD

12/7/2009 31

Impact of step size to convergence speedp p g p

Non summable diminishing1

=1.0Non-summable diminishing step size sequence:

0.7

0.8

0.9 1.0=2.0

k 0.5

0.6

Link

rate

kk

0.2

0.3

0.4

0 10 20 30 40 50 60 70 80 90 1000

0.1

Iteration No.

Iteration of a link rate in a 100-peer P2P VoD buffer-forwarding system

12/7/2009 32

Dynamics handling in P2P VoDy g

Convergence speed during transition3

76

-75

Convergence speed during transition

1

2

X2=2X3=3

1

4

X4=0

u2

78

-77

-76

ion

valu

e

1

5X1=5

u1

80

-79

-78

Dua

l fun

ct5

Steady state (flow conservation): x1=x2+x3Dual variable: u1=0.4, u2=0.5, u3=0.3

980 990 1000 1010 1020 1030 1040 1050 1060 1070 1080-81

-80

No. of iteration

Peer 3 left, peer 4 joined

Update dual variables: u1=0 6 u2=0 3 u4=0 1Dynamic 200-peer and 4-parent/peer scenario, 10 peers left and 20 new peers joined in the previous time slot

Update dual variables: u1 0.6, u2 0.3, u4 0.1

Update primal variables: x1=4, x2=3, x4=1Reach new steady state: x1=x2+x4

12/7/2009 33

Communication overhead for throughput maximization bl i b ff f diproblem in buffer-forwarding systems

ii vu ,3 4

2Link 1 Link 2

The dual variables at node i:The dual variables at link l: l

Th i l i bl (li k t ) t li k l x2

1Link 3

The primal variable (link rate) at link l: lxEach node is responsible for updating the primal and dual variables at this node

d t it t i li kand at its outgoing links

Node 2 computes the link rate at link 3: 2132133 1 vupx 2132133 1 vupxCommunication overhead: node 2 needs to request u from node 1 requestCommunication overhead: node 2 needs to request u1 from node 1, request λ1 from node 3, and λ2 from node 4

The update of dual variables requires the link rates of connected links, no communication overhead

ui: incoming link ratesvi: outgoing link rates

12/7/2009 34

λl: incoming link rates

A Hybrid-forwarding Architecture for y gP2P VoD

Server 1

130

116

104

43

22

3

7

130

134

34

115

27

12Video-1 buffer-forwarding 12

75

Buffer-forwarding link

Idle peers

Video-2 buffer-forwarding overlay

Video 1 buffer forwarding overlay

Hybrid-forwarding architectureShare both buffer and storage, to improve throughput

Storage-forwarding link

g p g pPeers contribute their stored segments to other peersImplement service differentiation among videosStored segments are stable robust to peer dynamics

12/7/2009 35

Stored segments are stable, robust to peer dynamics

Buffer-forwarding and storage-forwarding g g goverlay construction

1 Source node 1 Source node

St d t 22

3

Buffer2

Stored segment 2Requesting segment 4

33

4Peer 3

S

3


Playback progress10 15 20


Buffer-forwarding overlay Storage-forwarding overlay

12/7/2009 36

Throughput maximization in hybrid-

P bl f l ti

g p yforwarding architectureProblem formulation:

Aggregate weighted throughput 1 maximize pxa Ni Ll

llili

Aggregate throughput

Ll

lNi Ll

llili xpxa 21 minimize

Source rate constraint

Download bandwidth constraint,

, subject to

N i, Ixa

Ni, sxa

ilil

rLl

lil

Upload bandwidth constraint

Buffer-forwarding constraint, ,

,

Llxcx

Ni, Oxhxd

Blmlml

iLl

lilLl

lil

Ll

Buffer forwarding constraint

0

, ,

,,

Llx

LlFhx Sl

Niiill

lLm

mlml

Storage-forwarding constraint

Original optimization problem:Linear Programming (LP) problem

Converted to:Strictly convex optimization problem distributed algorithm to solve it

. ,0 Llxl

12/7/2009 37

Linear Programming (LP) problemStrictly convex optimization problem, distributed algorithm to solve it

Throughput maximization in hybrid-

Simulation results:

g p yforwarding architecture (cont.)Simulation results:

0 6

0.7Hybrid-forwarding centralizedHybrid-forwarding distributedBuffer-forwarding distributed

0.5

0.6ut

[Mbp

s]g

Storage-forwarding distributed

0.3

0.4

age

thro

ughp

u

0.1

0.2

Ave

ra

100 200 300 400 5000

Number of peers


12/7/2009 38


Other observation 1 – Number of Peers

Simulation results (Poisson distribution):Simulation results (Poisson distribution):

Buffer forwarding Hybrid forwarding

12/7/2009 39

Other observation 2 – Scalability

Simulation results:

y

Simulation results:

Comparison of the cost introduced by the proposed distributed algorithm in buffer-forwarding P2P VoD systems with different network sizes: (a)in buffer forwarding P2P VoD systems with different network sizes: (a) the number of the overlay links, (b) the average number of iterations per link, and (c) the average communication overhead per node

12/7/2009 40

Outline





Conclusions

12/7/2009 41

Wireless visual sensor networks Applications of WVSN Video sensor

Video surveillance, environmental tracking

The proposed distributed The proposed distributed algorithm: Maximize the network lifetime by

jointl optimi ing so rce ratesjointly optimizing source rates, encoding powers, and routing scheme

Total power consumption at a Sinkp pnode

fEncoding power + transmission power + reception power

A wireless visual sensor network

Network lifetime: defined as minimum node lifetime

12/7/2009 42

Related work Network Lifetime maximization for Wireless visual sensor

networks (WVSNs)networks (WVSNs) Conventional wireless sensor networks

Collect data (e.g., temperature), negligible power consumption on ( g ) g gsignal processing at sensor node

Existing distributed optimizations for conventional wireless sensor networkwireless sensor network Tradeoff between the source rate allocation and the network

lifetime [Nama2006] Distributed algorithm to maximize lifetime [Madan2006] These methods cannot be applied directly to WVSNs, since they

omit the processing power consumption at the sensor nodes Maximization of network lifetime for WVSN by jointly optimizing source

rates, encoding powers and routing scheme. [YHe-ICME07b, YHe-TCSVT2008a]

12/7/2009 43

Markov model and encoding powerg p

Channel error model01lq01lqChannel error model

Two-state Markov model0 1

lq011 lq 101 lq

0 1

lq011 lq 101 lq

A bit b bilit10lb qp

10lq10lq

Average bit error probability: ,0110ll

ll qq

p

packet loss rate (PLR): .11Gb

lpl pp p ( ) ll pp

Power consumption modelEncoding power consumptionEncoding power consumption

Power-rate-distortion model [He2006]3/22 Ps .2 shh Ps

sh ed

Under the same encoding power, increasing rate reducing distortion

12/7/2009 44

Under the same rate, increasing encoding power reducing distortion

Graph modelp

Network graphNetwork graphA WVSN is modeled by a directed graph G = (N, L)

otherwise.,0, node intolink incomingan is link if,1, node fromlink outgoingan is link if,1

ilil

ailMatrix A:

.otherwise,0, node fromlink outgoingan is link if,1 il

ailMatrix A+:

.otherwise,0, node intolink incomingan is link if,1 il

ailMatrix A-:

Each session follows the law of flow conservation:

, , , NiVhxa hihlil

12/7/2009 45

hiLl

hlil

Achievable maximum network lifetime Without loss Problem formulation:

iii i BTT i

Network lifetime

, , , :subject to

minmin maximize

NiVh xa

yacycaPTT

hiLl

hlil

Lllil

r

Lll

slilsi

iinet

Flow conservation

Network lifetime

, ,

, ,

3/2..2 VhDe

Llyx

hPs

Vhlhl

Ll

shh

Aggregate flow rate

Video quality requirement

. ,0 , ,0 , , ,0

VhPVhsLlVhx

shh

hl

Original optimization problem

12/7/2009 46

Achievable maximum network lifetime (cont.)

P bl i Problem conversion: minimize q

Flow conservation

Aggregate flow rate, ,

, , , :subject to

Llyx

NiVh xa

lhl

hiLl

hlil

Video quality requirement , ,

, ,//log 3/22

NiqByacycaP

VhsPD

ililr

lslilsi

hshh

Vh

Power constraint

. ,0 , ,0 , , ,0

VhPVhsLlVhx

shh

hl

LlLl

First conversion: change the variable: q=1/Tnet

12/7/2009 47

g q net

Problem conversion in wireless visual sensor networks (cont.)

P2 i i iq q1

P2:

.sconstraint :subject to

minimize q1

2

1

20q

23

qq

23

q3

q2

(q q q )P3:

bj t t

N minimize 2

NiVh

q

(q1=q2=q3)

P4::subject to

minimize 2

NiVhxa

qNi

i

Vh

hVh Ll

hl sx 22

//l

, ,

, , , :subject to

3/22 VhPD

Llyx

NiVh xa

Vhlhl

hiLl

hlil

, ,//log

, ,

, , , :subject to

3/22 VhsPD

Llyx

NiVhxa

hshh

Vhlhl

hiLl

hlil

, , ,0

, ,

, ,//log 3/2

LlVhx

NiqByacycaP

VhsPD

hl

iLl

lilr

Lll

slilsi

hshh

, , ,0

, ,0

, ,

LlVhx

Llqa

NiBqyacycaP

hl

Niiil

iiLl

lilr

Lll

slilsi

12/7/2009 48

. ,0 , ,0 VhPVhs shh . ,0 , ,0 ,,,

VhPVhsV

shh

hl

Achievable maximum network lifetime (cont.) Problem conversion:

minimize 222 sxqVh

hVh Ll

hlNi

i

Flow conservation

Video quality requirement , ,//log

, , , :subject to

3/22 VhsPD

NiVh xa

hshh

hiLl

hlil

VVN

,,0

, ,

Llqa

NiBqxacxcaP

iil

iiLl Vh

hlilr

Ll Vhhl

slilsi

Power constraint

Auxiliary variables are equal

. ,0 , ,0 , 0 , , ,0

, ,0

VhPVhsNi,qLlVhx

Llqa

shh

ihl

Niiil

Second conversion: introduce auxiliary variables: qi

12/7/2009 49

y qi

Achievable maximum network lifetime (cont.)

Si l ti lt Simulation results

12x 106

Source-and-Routing Optimized (Proposed) 0.7 Encoding power0.7 Encoding power

10

11

ork

lifet

ime

[s]

g p ( p )Source-Optimized Scheme (SOS)Routing-Optimized Scheme (ROS)

0.5

0.6

n [W

]

g pTransmission and reception power

0.5

0.6

n [W

]

g pTransmission and reception power

A B C

8

9

max

imum

net

wo

0.3

0.4

wer

con

sum

ptio

n

0.3

0.4

wer

con

sum

ptio

n

7

8

Ach

ieva

ble

m

0.1

0.2Pow

e

0.1

0.2Pow

e0 50 100 150 200 250 300

6

Encoding distortion requirement

Network lifetime comparison Power consumption at each node

0 5 10 15 20 25 30 350

Sensor node No.1 2 3 4 5 6 7 8 90 5 10 15 20 25 30 35

0

Sensor node No.1 2 3 4 5 6 7 8 9

12/7/2009 50

p Power consumption at each node

Network lifetime for large-delay g yapplications Large-delay applications:

Example: visual data 11

12x 106

Average PLR=0Average PLR=0.072Average PLR=0.134Average PLR=0.264

collection Retransmissions to recover

th t d k t9

10

twor

k lif

etim

e [s

]

the corrupted packets Retransmission consumes

extra power reduce 6

7

8

Max

imum

net

w

extra power, reduce network lifetime 0 50 100 150 200 250 300

5

6


N k lif i i h i iNetwork lifetime with retransmissions

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Network lifetime for small-delay yapplications Small-delay applications:

Example: real-time traffic 10

11

12x 106

[s]

Average PLR=0Average PLR=0.072Average PLR=0.134Average PLR=0.264

monitoring FEC to recover the

t d k t8

9

10

netw

ork

lifet

ime

corrupted packets Introduce extra encoding

and decoding power 5

6

7

Max

imum

n

and decoding power consumption, reduce network lifetime

0 50 100 150 200 250 3004


N k lif i i h FECnetwork lifetime

Network lifetime with FEC

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Outline





Conclusions

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Optimized video unicasting over wireless p gad hoc networks Video unicasting Video unicasting

From a source to a receiver Example: A group of visitors in a park,

Source Example: A group of visitors in a park,

person A receives video streaming from person B via relays.

Relay node The proposed optimized

video unicasting scheme

y

Prioritized coding + network coding Minimize the distortion by jointly

optimizing the source rate allocation

Receiver

Video unicastingoptimizing the source rate allocationand the routing scheme

Video unicasting

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Related work Video streaming over wireless ad hoc networks

Distributed optimization for data communications over Distributed optimization for data communications over wireless ad hoc networks [Xiao2004, Chen2006]

The optimizations for data communications cannot be The optimizations for data communications cannot be applied directly to real-time streaming applications

Existing optimizations for video streaming over wireless ad hoc networks Maximize the expected video quality using genetic

algorithm [Mao2004] (centralized thus high complexity)algorithm [Mao2004] (centralized thus high complexity) Optimization of resource competition among multiple

unicast video sessions [Zhu2006] (distributed and low l it t h d )complexity at each node)

Optimal resource allocation for both video unicast streaming [and video multicast streaming [YHe-ISCAS06, YHe-PCM07,

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[ g [ , ,YHe-TCSVT08b] over wireless ad hoc networks.

Video distortion model

180

200Akyio QCIF sequence

Fitting distortion modelExperimental data

Video distortion modelDistortion vs. received throughput

120

140

160

on

Modeled as:

0 Dd

60

80

100

Dis

torti

o d=2.97+ 474.97/(R+0.13),0

0

RDd

Convex function

0 50 100 150 200 250 3000

20

40Convex functionData fitting technique to find Parameters: 000 ,, D

Throughput [Kbps]

0D : Parameter related to encoding distortion

P t f t i i di t ti

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: Parameters for transmission distortion00 ,

Optimized video unicasting over wireless p gad hoc networks (cont.)

Problem formulation: Problem formulation:Video distortionFlow conservation

45

Flow conservation

Link capacity constraint

35

40

Original optimization problemConverted to:strictly convex optimization problem

20

25

30

PS

NR

[dB

]

strictly convex optimization problem, develop distributed algorithm to solve it

5

10

15

Proposed routingCongestion-minimized routingDouble-disjoint-path routing

Simulation results: Comparison of frame PSNR

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0 10 20 30 40 50 60 70 80 90 1005

Frame No.

Comparison of frame PSNR

Optimized video multicasting over p gwireless ad hoc networks Video multicasting:Video multicasting:

Streaming from a source node to H receivers simultaneously With network coding, a multicast flow = H conceptual

unicast sessions [Ahlswede2000]unicast sessions [Ahlswede2000] The proposed optimized multicasting scheme

Minimize the distortion by jointly optimizing the source rate allocation, the routing scheme and the powerrouting scheme and the power

11 1

Source node

11 1

Source node

11

Source node

1

2

4

3

110

0

2

4

3

001

1

2

4

3

111

1

56 711 0

Receiver 1 56 710 1

Receiver 2 56 711 1

Receiver 2Receiver 1

C C f

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(a) (b) (c)Conceptual unicast session 1 Conceptual unicast session 2 Multicast flow

Optimized video multicasting over p gwireless ad hoc networks Packet loss rate (PLR) in wireless ad hoc networks

Loss due to transmission error (pi2)

Loss due to congestion (p )

(pi2)

Loss due to congestion (pi1)

•PLR at link i: 21 111 iii ppp

where

TL

RCTTdelayobpi expexpPr1

ik

kikthi

PGPGSIRp

1

112

12/7/2009 59

iii PG

Optimized video multicasting over p gwireless ad hoc networks (cont.) Problem formulation: Problem formulation:

Total distortionAggregate throughputFlow conservationMulticast flow rate yl

Capacity constraint

gg g g p

p yLink capacity under CDMA

Transmit power constraint

O i i l i i i blOriginal optimization problemNetwork coding eliminates duplicate packets, change the objective function to maximize the aggregate throughput

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Optimized video multicasting over p gwireless ad hoc networks (cont.) Distributed solution using hierarchical dual decompositions Distributed solution using hierarchical dual decompositions

Optimization variables (s,x,y,P)Fi t l d l d iti

Network flow variables (s,x,y) Power variable (P)

First-layer dual decomposition

Second-layer dual decompositionSecond-layer decompositionusing game theory

Compute link rate and transmit power

Compute link rate and transmit power

for each outgoing link

Node 1 Node N

for each outgoing link

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Optimized video multicasting over p gwireless ad hoc networks (cont.) Simulation results:

035

0.4Single-treeDouble-treeOptimized035

0.4Uniform-powerOptimized

0.25

0.3

0.35

Mbp

s]

p

0.25

0.3

0.35

Mbp

s]

0.15

0.2

Thro

ughp

ut [M

0.15

0.2

Thro

ughp

ut [M

1 2 3 40

0.05

0.1

1 2 3 40

0.05

0.1

1 2 3 4

Receiver ID

1 2 3 4Receiver ID

Compare to uniform-power scheme Compare to tree-based schemes

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Optimized video multicasting over wireless ad hoc k

4

4.5

5

1.6

1.8

2

networks

1.5

2

2.5

3

3.5

Lam

da

0.6

0.8

1

1.2

1.4

Pow

er [W

]

0 7 0 3

0 50 100 150 200 250 300 350 400 450 5000

0.5

1

Iteration No.0 50 100 150 200 250 300 350 400 450 500

0

0.2

0.4

Iteration No.

0 4

0.5

0.6

0.7

e ra

te [M

bps]

0.2

0.25

0.3

te [M

bps]

0.1

0.2

0.3

0.4

Con

cept

ual s

ourc

e

0.05

0.1

0.15

Mul

ticas

t lin

k ra

t0 50 100 150 200 250 300 350 400 450 500

0

0.1

Iteration No.

0 50 100 150 200 250 300 350 400 450 500

0

Iteration No.

Optimization results for a 50-node wireless ad hoc network

12/7/2009 63

A multicast flow for a source to 8 receivers

References [Bertsekas2003] D. P. Bertsekas, A. Nedic, and A. E. Ozdaglar, Convex Analysis and Optimization,

Athena Scientific, 2003. [Boyd2004] S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004. [Do2004] T. T. Do, K. A. Hua, and M. A. Tantaoui, “P2VoD: Providing Fault Tolerant Video-on-Demand [ ] , , , g

Streaming in Peer-to-Peer Environment,” in Proc. of IEEE ICC, vol. 25, no. 1, pp. 119-130, Jan. 2004. [Li2006] Z. Li and A. Mahanti, “A Progressive Flow Auction Approach for Low-Cost On-Demand P2P

Media Streaming,” in Proc. of ACM QShine, Aug. 2006. [Yiu2007] W. P. Yiu, X. Jin and S. H. Chan, “VMesh: Distributed segment storage for peer-to-peer

interactive video streaming,” IEEE Journal on Selected Areas in Communications, vol. 25, no. 9, pp.interactive video streaming, IEEE Journal on Selected Areas in Communications, vol. 25, no. 9, pp. 1717-1731, Dec. 2007.

[YHe-ICASSP08] Y. He, I. Lee, and L. Guan, “Distributed throughput maximization in hybrid-forwarding P2P VoD applications”, in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2165─2168, Las Vegas, USA, April 2008.

[YHe-TMM2008a] Y He I Lee and L Guan “"Distributed throughput optimization in P2P VoD [YHe TMM2008a] Y. He, I. Lee and L. Guan, Distributed throughput optimization in P2P VoD systems,” IEEE Transactions on Multimedia, vol. 11, no. 3, pp. 509-522, April 2009..

[YHe-TMM2008b] Y. He, G. Shen, Y. Xiong and L. Guan, “Optimal prefetching scheme in P2P VoD applications with guided seeks,” IEEE Transactions on Multimedia, vol. 11, no. 1, pp. 138-151, Jan. 2009.

[Wu2005] C Wu and B Li “Optimal Peer Selection for Minimum-Delay Peer-to-Peer Streaming with [Wu2005] C. Wu and B. Li, Optimal Peer Selection for Minimum-Delay Peer-to-Peer Streaming with Rateless Codes,” in Proc. of ACM MM, pp. 69-78, Nov. 2005.

[YHe-ICME07a] Y. He, I. Lee, and L. Guan, “Distributed rate allocation in p2p streaming”, in Proc. of IEEE International Conference on Multimedia & Expo (ICME), Special Session on P2P Multimedia Content Access and Distribution, pp. 388─391, Beijing, China, July 2007.

[Xiao2004] L Xiao M Johansson and S Boyd “Simultaneous routing and resource allocation via dual [Xiao2004] L. Xiao, M. Johansson and S. Boyd, Simultaneous routing and resource allocation via dual decomposition,” IEEETransactions on Communications, vol. 52, no. 7, pp. 1136-1144, Jul. 2004.

[Chen2006] L. Chen, S. H. Low, M. Chiang, and J. C. Doyle, “Cross-layer congestion control, routing and scheduling design in ad hoc wireless networks,” in Proc. of IEEE INFOCOM, pp. 1-13, Apr. 2006.

[Mao2004] S. Mao, X. Cheng, Y. T. Hou, and H. D. Sherali, “Multiple description video multicast in wireless ad hoc networks ” in Proc of IEEE BROADNETS pp 671 680 Oct 2004

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wireless ad hoc networks, in Proc. of IEEE BROADNETS, pp. 671-680, Oct. 2004.

References (cont.)( ) [Zhu2006] X. Zhu, J. P. Singh, and B. Girod, “Joint routing and rate allocation for multiple video streams

in ad hoc wireless networks,” Journal of Zhejiang University, Science A, vol. 7, no. 5, pp. 727-736, May 2006.[YH ISCAS06] Y H I L d L G “O ti i d lti th ti i d l d iti f [YHe-ISCAS06] Y. He, I. Lee, and L. Guan, “Optimized multi-path routing using dual decomposition for wireless video streaming”, in Proc. of IEEE International Symposium on Circuits and Systems (ISCAS), pp. 977─980, New Orleans, USA, May 2007.

[YHe-PCM07] Y. He, I. Lee, and L. Guan, “Video multicast over wireless ad hoc networks using distributed optimization”, in Proc. of Pacific-Rim Conference on Multimedia (PCM), pp. 296─305, Hongkong Dec 2007Hongkong, Dec. 2007.

[Nama2006] H. Nama, M. Chiang, and N. Mandayam, “Utility-lifetime trade-off in self-regulating wireless sensor networks: A cross-layer design approach,” in Proc. of IEEE ICC, vol. 8, pp. 3511-3516, Jun. 2006.

[Madan2006] R. Madan, S. Lall, “Distributed algorithms for maximum lifetime routing in wireless sensor networks,” IEEE Transactions on Wireless Communications, vol. 5, no. 8, pp. 2185-2193, Aug. 2006.

[YHe-ICME07b] Y. He, I. Lee, and L. Guan, “Network lifetime maximization in wireless visual sensor networks using a distributed algorithm”, in Proc. of IEEE International Conference on Multimedia & Expo (ICME), pp. 2174─2177, Beijing, China, July 2007.

[He2006] Z. He and D. Wu, “Resource allocation and performance analysis of wireless video sensors,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 5, pp. 590-599, May 2006.

[Ahlswede2000] R. Ahlswede, N. Cai, S.-Y. R. Li and R. W. Yeung, “Network information flow,” IEEE Transactions on Information Theory, vol. 46, pp. 1204-1216, Jul. 2000.

[YHe-TCSVT2008a]Y. He, I. Lee and L. Guan, “Distributed algorithms for network lifetime maximization in wireless visual sensor networks,” IEEE Transactions on Circuits and Systems for Video Technology, vol.19, no. 5, pp. 704-718, May 2009. pp y

[YHe-TCSVT2008b] Y. He, I. Lee and L. Guan, “Optimized video multicast in wireless ad hoc network using network coding,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 6, pp. 796-807, June 2009..

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Thank You!Thank You!

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