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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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