Overlay Multicasting for Quality Video Delivery

1. Differentiated Quality Video Delivery in Overlay Multicasting Environment Ying Qiao Carleton University Project Presentation at the class: Quality of Service Management for Multimedia Applications Provided by: Professor Bochmann

2. Outline • Introduction -- Internet multimedia delivery -- Types of Video service -- multimedia multicast • Overlay multicast environment -- Video coding -- Video delivery • Layered Peer-to-Peer Streaming • Supporting Large-Scale Live Streaming Applications with Dynamic Application End-Points • Incentive mechanism for Peer-to-Peer Media Streaming • Conclusion

3. Introduction (1) • Internet media delivery • Types of Video Service -- No VOD -- Pay-Per-view -- True VOD -- Near VOD (NVOD) -- Quasi-VOD (QVOD) • Basic multicast functionality -- Group membership management -- Data delivery path maintenance -- Replication and forwarding

4. Introduction (2) • Internet media multicast • IP multicast • Overlay multicast Ref 4

5. Overlay Multicasting Environment (1) • Resources provided by peer end node -- Network bandwidth -- Storage space -- CPU power • Features -- Overlay Multicast is deployed with the basic uni-cast routing infrastructure -- End hosts only maintain state for the groups they are participating in

6. Overlay Multicasting Environment (2) • Three architectures -- Dedicated-Infrastructure -- Application-Endpoint -- Waypoint [Ref 4]

7. Overlay Multicasting Environment (3) • Video Coding -- Replicated streaming -- Layered streaming -- Multiple Description Coding [Ref 1]

8. Overlay Multicasting Environment (4) • Video Delivery tree -- Single tree -- Multiple tree ZIGZAG [Ref 5] SplitStream [Ref 6]

9. Overlay Multicasting Environment (5) • Challenge for overlay multicast -- Bandwidth constraints -- Receiver scalability -- Network dynamics -- Receiver heterogeneity [Ref 4]

10. Layered Peer-to-Peer Streaming (1) • Layered video [Ref 2] -- Video is encoded into one base layer and multiple enhancement layers -- The base layer can be decoded independently -- The enhancement layers can be decoded cumulatively • Network heterogeneity [Ref 3]

11. Layered Peer-to-Peer Streaming (2) • Large-scale on-demand multimedia distribution -- Asynchrony of user requests -- Heterogeneity of client resource capabilities • Layered Peer-to-Peer Streaming -- Cache-and-relay -- Layer-encoded streaming

12. Layered Peer-to-Peer Streaming (3) • Layered Peer-to-Peer Streaming -- Cache-and-relay -- Layer-encoded streaming • Goal -- Maximize the number of the received streams from end nodes other than the source -- Subject to (1) number of received streams for one receiver <= inbound bandwidth of the receiver (2) total number of received streaming from one sender <= outbound bandwidth of the sender

13. Basic Algorithm • Receiver k, inbound bandwidth • a set of the hosts qualified as the supplying peers of and sorted the Hosts with the available layers • Arranging the layers from the beginning of S

14. Performance Evaluation (1) • Request composition: -- Modem/ISDN peers, 50%, 112kbps -- Cable Modem/DSL peers, 35%, 1Mbps -- Ethernet Peers, 15%, 10Mbps • Quality satisfaction -- The ratio of received quality and expected quality of a peer • Result --The layered approach is able to fully utilize the marginal outbound bandwidth of supplying peer, and more adapted to the bandwidth asymmetric

15. Performance Evaluation (2) • Longer buffer enables a supplying peer to help more later-coming peers by prolonging the supplying chain • Further increasing buffer size has very little help at prolonging the supplying chain • Request chain (tree) in both cases • Layered approach relieves the server bandwidth request with peer bandwidth

16. Fairness • Outbound/inbound < 1 • Outbound/inbound >=1 • 40% Ethernet Peers are not fully satisfied • Reason: the limiting inbound of the Modem/ISDN, and Cable Modem/DSL peers can not satisfied the Ethernet Peers

17. Robustness • Robustness -- 50% of the supplying peers depart early before the playback is finished -- Reconfiguration through buffer -- Failure ratio is the percentage of failed peers among all departure peers

18. Conclusion for the layered Peer-To-Peer Streaming • Be optimal at maximizing the streaming quality of heterogeneous peers • Be scalable at saving server bandwidth • Be efficient at utilizing bandwidth resource of supplying peers • Evaluation -- Whether establishing fairness among peers, in terms of streaming quality satisfaction and bandwidth contribution -- Whether being robust against unexpected peer departures/failures

19. Supporting Large-Scale Live Streaming Applications • Key requirements -- Resource constraints -- Stability -- Efficient overlay structure • Live Streaming Workload -- Large scale: the peak group size is 1,000 to 80,000 hosts -- A large number of short participations -- Heavy tail with some very long participations

20. Bandwidth Resource Constraints • Single Tree Protocols -- Resource Index: -- Trace study shows sufficient bandwidth resource • Multiple Tree Protocol -- Increase the overall resilience -- Tightly coupled with specialized video encoding -- Resource Index: SupplyOfBW/DemandOfBW -- Increase the supply of the resources

21. Stability (1) • Metrics -- Mean interval between ancestor change for each participation -- Number of descendants of a departing participation • Simulation of single tree -- Host join: asks the source to get m current group members, picks one host as parent -- Host leave: all of its descendants pick one host -- Parent Selection Algorithms: Oracle; Longest-First; Minimum depth; Random • Simulation Results -- Oracle is the best -- Minimum depth tree can provide good performance

22. Stability (2) • Simulation Results -- Oracle is the best -- Minimum depth tree can provide good performance

23. Stability (3) • Impact of Multiple-Tree Protocols -- Independent trees -- Load balancing -- Preemption • Simulation result -- More frequent ancestor changes -- Improved performance comes at a cost of more frequents disconnects, more protocol overhead, and more complex protocols

24. Efficient overlay structure (1) • Overlay structure closely reflects the underlying IP network -- Need to discover other nearby hosts as parents -- Partition hosts into clusters -- One member of each cluster is designated as the clustered head -- Hosts in the same cluster maintain knowledge about one another • Clustering Quality Metric -- Average and maximum intra-cluster distance in milliseconds

25. Efficient overlay structure (2) • Sensitivity to Number of Clusters -- More clusters smaller intra-cluster distance -- Maximum intra-cluster distance more sensitive to the change of number of clusters

26. Efficient overlay structure (3) • Sensitivity to Cluster Size and Resource Maintenance -- Bounding the cluster size doesn’t significantly affect the intra-cluster distances

27. Conclusion for large-scale live streaming applications with dynamic application end-points • Minimizing depth in single-tree protocols provides good stability performance • Multiple-tree protocols can significantly improve the quality of streams • Simple clustering techniques improve the efficiency of the overlay structure • Opening issue: encourage application end-points to contribute their resources is an important direction

28. Incentive Mechanism for Peer-to-Peer Media Streaming (1) System quality is: T is the total number of the packets in a streaming session, is 1 if the packet i arrives at the receiver before its scheduled play-out time, and 0 otherwise Cooperation brings quality Simultaneous uploading hurts quality

29. Incentive Mechanism for Peer-to-Peer Media Streaming (2) • Random peer selection provides random quality

30. Score-based incentive mechanism • Peer selection scheme allows a user to select peers with equal or lower rank to serve as suppliers • A user wishes to receive better-than-best-effort streaming, it must earn a positive score by contributing to the system • The stream quality for a receiver can be expressed as a function of contribution, score, or rank

31. Functions Scoring function: could be: Contribution cost: Rank Computation: Quality function:

32. Experiment system

33. Performance evaluation • Expected rate: the total bytes coming from all senders • The gain increases for the incentive when the K increases • When k>20, the difference of the rates decreases because the bottleneck is shifted from the hosts to the network • Packets the miss their play-out deadlines are considered as lost

34. Quality of Streaming

35. Conclusion for incentive mechanism for Peer-to-Peer Media Streaming • Motivation -- The stream quality is poor if the level of cooperation is low -- Cooperation from a few altruistic users cannot provide high quality streaming to its users in a large system • Conclusion -- A rank-based incentive mechanism achieves cooperation through service differentiation -- The contribution of a user is converted into a score, then the score is mapped into a rank, and the rank provides flexibility in peer selection that determines the quality of a streaming session -- Cooperative users earn higher rank by contributing their resources to others, and eventually receive high quality streaming

36. Conclusion • Application layer multicasting • Consuming the other end node’s resource while sharing own resource out • The differentiated quality is realized with replicated streaming, layered streaming, and MDC • Replicated streaming is used at the single tree delivery • In the single tree, the minimize depth algorithm shows good performance • Layered Streaming and MDC with multiple tree delivery increases resource, and improve the stability as well • Cluster can improve the efficiency of the overlay structure • Fairness is still an open issue • Incentive mechanism is a solution to encouraging resource sharing

37. Reference [1] Layered Peer-to-Peer Streaming [2] A Comparison of Layering and Stream Replication Video Multicast Schemes [3] Receiver-Driver layered Multicast [4] Internet Multicast Video Delivery [5] ZIGZAG: An Efficient Peer-to-Peer Scheme for Media Streaming [6] SplitStream: High-bandwidth content distribution in cooperative environment [7] The Feasibility of Supporting Large-Scale Live Streaming Applications with Dynamic Application End-Points [8] Incentive Mechanism for Peer-to-Peer Media Streaming

38. Appendix: Receiver-Driven Layered Multicast • Rate-adaptation protocol • Each receiver runs the control loop: -- On congestion, drop a layer -- On spare capacity, add a layer • Join-experiment -- adding layers at “well-chosen” times -- causing congestion, then the receiver drops the adding layers -- successful, the receiver start adding another join-experiment • Exponential Join timer for RLM adaptation at the join experiment • “Sharing learning” in multiple receivers for scaling of the receiver