Selection Strategies for Peer-to-Peer 3D Streaming

1. Selection Strategies for Peer-to-Peer 3D Streaming Wei-Lun Sung, Shun-Yun Hu, Jehn-Ruey Jiang National Central University, Taiwan 2008/05/29

2. Virtual environments (VE) • VEs allow users to interact in synthetic worlds • Larger content & more worlds  content streaming (i.e., 3D streaming) becomes necessary National Central University, Taiwan

3. 3D streaming • Continuous and real-time delivery of 3D content to allow user interactions without a full download. • Object streaming fragments mesh into base & refinements Refinements Base 1 2 3 (Hoppe 96) User National Central University, Taiwan

4. Scene streaming • multiple objects • object selection & prioritization [Teler & Lischinski 2001] National Central University, Taiwan

5. Comparison with media streaming • Highly interactive (latency-sensitive) • Behavior-based (non-linear) How to scale to millions of concurrent users? National Central University, Taiwan

6. Imagine you start with a globe National Central University, Taiwan

7. Zoom in… National Central University, Taiwan

8. To a city National Central University, Taiwan

9. and a building National Central University, Taiwan

10. Right now it’s flat… National Central University, Taiwan

11. But in the near future… National Central University, Taiwan

12. Observation • Limited & predictable area of interest (AOI)‏ • Overlapped visibility = shared content National Central University, Taiwan

13. Benefits of peer-to-peer • Scalable • Growing amount of total resources • Affordable • Commodity PCs • Feasible • Better client hardware (CPU, broadband networks)‏ • Availability of user-hosted machines National Central University, Taiwan

14. Peer selection • Choose suitable candidates so that content retrieval can be done quickly and efficiently • Source discovery • Which peers possess the needed data • Source selection • Which peers to request the data National Central University, Taiwan

15. Related Work: FLoD [Infocom 2008] • VE partitioned into cells with scene descriptions • Assumes P2P overlay that provides AOI neighbors star: self triangles: neighbors circle: AOI rectangles: objects National Central University, Taiwan

16. Peer selection in FLoD • Source discovery • Query-response • Extra delay due to queries • Source selection • Random selection • Requests contention due to overlapping requests National Central University, Taiwan

17. Request contention problem Overlapping requests create contentions R6 R1 R5 R2 R4 R1,R2,R3,R4,R5,R6 OBJ R3 R1,R2 R1,R2,R3 National Central University, Taiwan

18. Proposed Solutions National Central University, Taiwan

19. Incremental Piece List Exchange • Proactive notification of content availability • Periodic incremental exchange of content availability information with neighbors. incremental content information Msg_Type Obj_ID Max_PID Obj_ID Max_PID ‧‧‧‧ National Central University, Taiwan

20. Extended Candidate Buffer • Non-AOI neighbors may still possess data • Maintain extra list of non-AOI neighbors S R Obj National Central University, Taiwan

21. Multi-Level AOI Request • Localized requests may prevent contentions • Peers request from closer neighbors/levels first National Central University, Taiwan

22. Simulation Environment • Based on FLoD (available on SourceForge) • World size: 1000 x 1000 • Simulation steps: 3000 • Objects: 500 • Nodes: 50 ~ 500 (50 nodes increase) • AOI radius: 75 • Server bandwidth: 10 Mbps / 10 Mbps • Peer bandwidth: 1 Mbps / 256 Kbps National Central University, Taiwan

23. Simulation Environments (cont.) • Source discovery • (QR) query-response: 5 steps interval, 10 requests • (EE) exchanged & extended: 150 radius • Source selection • (RAND) random • (ML) multi-level AOI request : 4 levels • Original FLoD: QR-RAND • Proposed method: EE-ML National Central University, Taiwan

24. Hit Ratio National Central University, Taiwan

25. Base Latency National Central University, Taiwan

26. Fill ratio National Central University, Taiwan

27. Bandwidth (Server) National Central University, Taiwan

28. Bandwidth (Clients source discovery) National Central University, Taiwan

29. Conclusion • New selection strategies for P2P 3D streaming • Availability info exchange & extended candidate bufferreduce both latency and bandwidth overhead • multi-level AOI requestsobtain data from closer providers but improve only hit ratio • Future work • More sources • Physical topology • Pre-fetching National Central University, Taiwan

30. Q & A National Central University, Taiwan

31. Neighbor discovery via VON Voronoi diagrams identify boundary neighbors for neighbor discovery Non-overlapped neighbors Boundary neighbors New neighbors [Hu et al. 06] National Central University, Taiwan

32. ‧ ‧ ‧ ‧ ‧ LODDT Object Tree Node Aura U National Central University, Taiwan

33. LODDT (cont.) Candidates Requests • Discovery • Estimation • Selection • Every peer samples the time-to-serve (TTS) of its neighbors • Requestors organize their data requests so as obtain tree nodes in the right order • Drawback: incorrect estimation, congestion National Central University, Taiwan

34. Simulation Environments (cont.) • System performance • Hit ratio: Ratio of successful requests peers have sent • Latency: Duration between initial request and data arrival • Fill ratio: Ratio of the possessed required data • Scalability metrics • Bandwidth usage (consumption) • Content discovery overhead National Central University, Taiwan