Adaptive Peer-to-Peer Live Video Streaming

Luan, Tom H.

Sept. 2007

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Outline

1 Introduction of Peer-to-Peer Live Video Streaming

2 Adaptive Peer-to-Peer Live Video Streaming

3 Simulation

4 Conclusion

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IntroductionPeer-to-Peer Networks

Self-organized Overlay Network on-top of IP Networks with

Large scale network sizeDistributed and dynamic user behaviorHeterogenous bandwidth capacity

Peer-to-Peer live video streaming

Collaborative content forwarding among peersExample: PPLive, CoolStream, PPStream, TVants

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IntroductionPeer-to-Peer Networks

Self-organized Overlay Network on-top of IP Networks with

Large scale network sizeDistributed and dynamic user behaviorHeterogenous bandwidth capacity

Peer-to-Peer live video streaming

Collaborative content forwarding among peersExample: PPLive, CoolStream, PPStream, TVants

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IntroductionPeer-to-Peer Networks

Self-organized Overlay Network on-top of IP Networks with

Large scale network sizeDistributed and dynamic user behaviorHeterogenous bandwidth capacity

Peer-to-Peer live video streaming

Collaborative content forwarding among peersExample: PPLive, CoolStream, PPStream, TVants

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Research Issue

Due to dynamic and heterogeneity of the network,downloading performance can hardly be guaranteed

Users have stringent QoS requirements

Research Problem Statement

How to provide users with guaranteed QoS in the dynamic andheterogenous overlay network

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Adaptive P2P Live Video Streaming

Full bandwidth utilization with best delivered video quality

r : video playback rated : downloading rate of peers, d ≥ rMaximize r with limited overall bandwidth (Maxmin Problem)

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Adaptive P2P Live Video Streaming

Full bandwidth utilization with best delivered video quality

r : video playback rated : downloading rate of peers, d ≥ rMaximize r with limited overall bandwidth (Maxmin Problem)

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Adaptive P2P Live Video Streaming

Full bandwidth utilization with best delivered video quality

r : video playback rated : downloading rate of peers, d ≥ rMaximize r with limited overall bandwidth (Maxmin Problem)

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Overlay Topology Construction

Topology Formation Problem: How to connectpeers in an efficient graph

2N topology candidates are available (N: number ofnodes)Integer Programming problem (NP-complete)

Link-level Homogenous Overlay Graph

Peers are heterogenous, if

Overloaded, throttle the QoS of downstream nodesUnder-utilized, waste the bandwidth for best videoquality

Construct a homogenous network in terms ofoverlay connections

All the connections have equal bandwidthVideo flows DO NOT suffer from bottlenecksResource allocation is equivalent to allocatedownloading connections

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Overlay Topology Construction

Topology Formation Problem: How to connectpeers in an efficient graph

2N topology candidates are available (N: number ofnodes)Integer Programming problem (NP-complete)

Link-level Homogenous Overlay Graph

Peers are heterogenous, if

Overloaded, throttle the QoS of downstream nodesUnder-utilized, waste the bandwidth for best videoquality

Construct a homogenous network in terms ofoverlay connections

All the connections have equal bandwidthVideo flows DO NOT suffer from bottlenecksResource allocation is equivalent to allocatedownloading connections

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Assumptions and Notations

Assumption:

Peers can always download the desired video content fromconnected parent nodes

Random Linear Network Coding (RLNC)Multicast Tree formation

Bandwidth bottleneck is at the first hop on the uploading side,rather than the core of IP networks or the downloading side

Notations:

G = {V ,E} : overlay graph with V denoting peerset and E denoting overlay connectionsci : uploading bandwidth of peer i ∈ VOi : outgoing degree or fanout of peer i

Link-level Homogeneity ∀i ∈ V , ciOi

= Constant

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Centralized Algorithm

Suppose that a central controller is available

Peers are ranked in a descending order as{ ci1

Oi1,

ci2Oi2

, · · ·}1×|V | , withci1Oi1

≥ ci2Oi2

≥ · · ·When a new node joins, it downloads from the first m peers inthe list

Convergence to link-level homogeneous propelled by nodearrivals and departures

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Centralized Algorithm

Suppose that a central controller is available

Peers are ranked in a descending order as{ ci1

Oi1,

ci2Oi2

, · · ·}1×|V | , withci1Oi1

≥ ci2Oi2

≥ · · ·When a new node joins, it downloads from the first m peers inthe list

Convergence to link-level homogeneous propelled by nodearrivals and departures

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Centralized Algorithm

Suppose that a central controller is available

Peers are ranked in a descending order as{ ci1

Oi1,

ci2Oi2

, · · ·}1×|V | , withci1Oi1

≥ ci2Oi2

≥ · · ·When a new node joins, it downloads from the first m peers inthe list

Convergence to link-level homogeneous propelled by nodearrivals and departures

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Centralized Algorithm

Suppose that a central controller is available

Peers are ranked in a descending order as{ ci1

Oi1,

ci2Oi2

, · · ·}1×|V | , withci1Oi1

≥ ci2Oi2

≥ · · ·When a new node joins, it downloads from the first m peers inthe list

Convergence to link-level homogeneous propelled by nodearrivals and departures

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Centralized Algorithm

Suppose that a central controller is available

Peers are ranked in a descending order as{ ci1

Oi1,

ci2Oi2

, · · ·}1×|V | , withci1Oi1

≥ ci2Oi2

≥ · · ·When a new node joins, it downloads from the first m peers inthe list

Convergence to link-level homogeneous propelled by nodearrivals and departures

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Distributed Algorithm

Distribution Algorithm

Selection of peers based on probability

Random walk based on the Metropolis-Hastings algorithm(Randomized Sampling)

Distributed algorithm relies on local information only

Peers’ capacity per out-degree converges to global equilibriumvalue

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Protocol

1 Nodes Join

Select m peers based on using either the Centralized algorithmor the Distributed algorithm, where m is a constant

2 Nodes Depart

For peers who lose the parent nodes, re-establish oneconnection using the Centralized algorithm or the Distributedalgorithm

In the resulting network,

Each node maintains m downloading connectionsAchieve guaranteed bandwidth with

d = m×min{ c

O} = m× δ

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Protocol

1 Nodes Join

Select m peers based on using either the Centralized algorithmor the Distributed algorithm, where m is a constant

2 Nodes Depart

For peers who lose the parent nodes, re-establish oneconnection using the Centralized algorithm or the Distributedalgorithm

In the resulting network,

Each node maintains m downloading connectionsAchieve guaranteed bandwidth with

d = m×min{ c

O} = m× δ

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Protocol

1 Nodes Join

Select m peers based on using either the Centralized algorithmor the Distributed algorithm, where m is a constant

2 Nodes Depart

For peers who lose the parent nodes, re-establish oneconnection using the Centralized algorithm or the Distributedalgorithm

In the resulting network,

Each node maintains m downloading connectionsAchieve guaranteed bandwidth with

d = m×min{ c

O} = m× δ

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Adaptive Playback Rate Control

As the server, denoted by s, also converges, it

Measures its own capacity per out-degree

δs =Cs

Os

Adjusts the video playback rate as

r = m · δs

Layered video coding with Progressive Fine Granular Scalable(PFGS) video

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Simulation Setup

Peers Traffic:

Poisson distribution withmean rate λ = 10peers/sExponential life time withmean 1

µ

Overall departing rate is Nµ

Balance when λ = Nµ

50,000 peers are inserted andN = 10, 000 peers in thenetwork on average

Each simulation result is theaverage of 10 simulation runs

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Performance Evaluation

Capacity per out-degreedistribution of peers at timet = 3500s using DistributedAlgorithm

Capacity per out-degreedistribution of peers versus nodeindex at time t = 3500s usingDistributed Algorithm

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Performance Evaluation

Capacity per out-degreedistribution of peers at timet = 3500s using different peerselection scheme

Local Greedy Selection: selectpeers with largest capacity perout-degree among neighbors

Capacity per out-degreedistribution of peers versus nodeindex at time t = 3500s usingCentralized Algorithm

[3] T. Small, B. Li, B. Liang, ”Outreach: Peer-to-Peer TopologyConstruction towards Minimized Server Bandwidth Costs”, inIEEE JSAC, Special Issue on Peer-to-Peer Communications andApplications, January 2007

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Performance Evaluation

Downloading rate of peers when 35000 peers are inserted andthe playback rate tuned by the server

Optimal playback rate r = ∑i∈V Ci

|V | [14]

[14] R. Kumar, Y. Liu, K. W. Ross, ”Stochastic Fluid Theory for P2P Streaming Systems”, In Proc. of IEEEInfocom, Anchorage, Alaska, 2007

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Performance Evaluation

Investigate a specific peer with node index 1000 and infinitelife time, and examine its performance

Change a node’s uploadingcapacity and investigate itsout-degree evolution

Downloading rate of theinvestigated peer

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Adaptive Playback Rate Control -Performance Evaluation

The changes of playback rate with changing bandwidthdistribution of peers [10]

At t = 2000s, the capacity of joining peers is doubled[10] X. Hei, C. Liang, J. Liang, Y. Liu, K. W. Ross, ”A Measurement Study of a Large-Scale P2P IPTV System”,in IEEE Transactions on Multimedia, 2007

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Conclusion

Adaptive video streaming to achieve full bandwidth utilizationand best delivered video quality

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References I

D. Carra, ”Performance Evaluation of Overlay Content Distribution Systems”, Ph.D. Thesis of University of

Trento, March. 2007

N. Magharei, R. Rejaie, ”Understanding Mesh-based Peer-to-Peer Streaming”, in Proc. of Nossdav, May

2006

T. Small, B. Li, B. Liang, ”Outreach: Peer-to-Peer Topology Construction towards Minimized Server

Bandwidth Costs”, in IEEE JSAC, Special Issue on Peer-to-Peer Communications and Applications, January2007

V. Venkataraman, K. Yoshida, P. Francis, ”Chunkyspread: Heterogeneous UnstructuredTree-Based

Peer-to-Peer Multicast”, in the Proc. of ICNP, Nov 2006

Y. Chu, S. G. Rao, S. Seshan, and H. Zhang,”A Case for End System Multicast”,in IEEE JSAC, Special

Issue on Networking Support for Multicast, No. 8, 2002

P. Francis, ”Yoid: Extending the Internet MulticastArchitecture”, http://www.icir.org/yoid

M. Castro, P. Druschel, A. M. Kermarrec, A. Nandi,A. Rowstron, A. Singh, ”Splitstream:High-Bandwidth

Multicast in CooperativeEnvironments”, in Proc. of SOSP, 2003

V. N. Padmanabhan, K. Sripanidkulchai, ”The Case for Cooperative Networking”, in Proc. of IPTPS, 2002

X. Zhang, J. Liu, B. Li, T.-S. P. Yum, ”CoolStreaming/DONet: A Data-driven Overlay Network for

Efficient Live Media Streaming”, in Proc. of IEEE INFOCOM, 2005

X. Hei, C. Liang, J. Liang, Y. Liu, K. W. Ross, ”A Measurement Study of a Large-Scale P2P IPTV

System”, in IEEE Transactions on Multimedia, 2007

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References II

J.W. Byers, M. Luby, M. Mitzenmacher, ”A DigitalFountain Approach to Asynchronous Reliable

Multi-cast”, in IEEE JSAC,Special Issue on Network Support for Multicast Communications, 2002

N. Magharei, R. Rejaie, Y. Guo , ”Mesh or Multiple-Tree: A Comparative Study of Live P2P Streaming

Approaches”, in Proc. of IEEE INFOCOM, May 2007

Y. L. Pavlov, ”Random Forests”, Utrecht, VSP, 2000

R. Kumar, Y. Liu, K. W. Ross, ”Stochastic Fluid Theory for P2P Streaming Systems”, Infocom, Anchorage,

Alaska, 2007

C. Huang, J. Li, K.W. Ross, ”Can Internet VoD be Profitable?”, In Proc. of ACM Sigcomm, Kyoto, 2007

L. Guo, S. Chen, Z. Xiao, E. Tan, X. Ding, X. Zhang, ”Measurements, analysis, and modeling of

BitTorrent-likesystems”, In Proc. of ACM IMC, 2005

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Topology Formation - DistributedAlgorithm

Random Sampling

For a peer j who wants to set up a new connection, it willsample a peer i ∈ V with probability

πi =C2

iOi

∑x∈VC2

xOx

Z = ∑x∈Vc2x

kxis unknown

Decentralized RandomWalk Approach

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Topology Formation - DistributedAlgorithm

Random Sampling

For a peer j who wants to set up a new connection, it willsample a peer i ∈ V with probability

πi =C2

iOi

∑x∈VC2

xOx

Z = ∑x∈Vc2x

kxis unknown

Decentralized RandomWalk Approach

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Topology Formation - DistributedAlgorithm

Random Sampling

For a peer j who wants to set up a new connection, it willsample a peer i ∈ V with probability

πi =C2

iOi

∑x∈VC2

xOx

Z = ∑x∈Vc2x

kxis unknown

Decentralized RandomWalk Approach

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Topology Formation - DistributedAlgorithm

Random Sampling

For a peer j who wants to set up a new connection, it willsample a peer i ∈ V with probability

πi =C2

iOi

∑x∈VC2

xOx

Z = ∑x∈Vc2x

kxis unknown

Decentralized RandomWalk Approach

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Topology Formation - Random WalkAlgorithm

Joining Procedure

When a new peer x0 joins the network

1 Pick a peer list from theRand Point

2 Choose m peers in thepeer list and Issue onewalker to each of them

3 Walkers are routed frompeer xn to peer xn+1 withprobability P(xn+1|xn),where 0 ≤ n < TTL

4 Select peer xTTL

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Topology Formation - Random WalkAlgorithm

Joining Procedure

When a new peer x0 joins the network

1 Pick a peer list from theRand Point

2 Choose m peers in thepeer list and Issue onewalker to each of them

3 Walkers are routed frompeer xn to peer xn+1 withprobability P(xn+1|xn),where 0 ≤ n < TTL

4 Select peer xTTL

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Topology Formation - Random WalkAlgorithm

Joining Procedure

When a new peer x0 joins the network

1 Pick a peer list from theRand Point

2 Choose m peers in thepeer list and Issue onewalker to each of them

3 Walkers are routed frompeer xn to peer xn+1 withprobability P(xn+1|xn),where 0 ≤ n < TTL

4 Select peer xTTL

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Topology Formation - Random WalkAlgorithm

Joining Procedure

When a new peer x0 joins the network

1 Pick a peer list from theRand Point

2 Choose m peers in thepeer list and Issue onewalker to each of them

3 Walkers are routed frompeer xn to peer xn+1 withprobability P(xn+1|xn),where 0 ≤ n < TTL

4 Select peer xTTL

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