Peer-to-Peer 3D Streaming ACM Multimedia 2007 submission Presenter: Shun-Yun Hu ( 胡舜元 ) Adaptive Computing and Network Lab Dept. of CSIE, National Central University 2007/04/17

2/ Adaptive Computing and Networking Lab, CSIE, NCU Outline Introduction P2P-based 3D Scene Streaming Design of FLoD Prototype Implementation Simulation Evaluation Conclusion

3/ Adaptive Computing and Networking Lab, CSIE, NCU Introduction The problem  The scalability of 3D scene streaming  All 3D streaming currently adopts client-server Our solution  Peer-to-peer (download contents from clients)  Clients have shared visibility / contents in a scene

4/ Adaptive Computing and Networking Lab, CSIE, NCU What is 3D streaming? Continuous and real-time delivery of 3D contents over network connections to allow user interactions without a full download. Contents are fragmented, transmitted, reconstructed, then displayed. 4 types: object, scene, visualization, image-based

5/ Adaptive Computing and Networking Lab, CSIE, NCU Object streaming Hoppe 1996 Progressive Meshes

6/ Adaptive Computing and Networking Lab, CSIE, NCU Scene streaming Many objects Remote walk- through Object selections & transmissions Teler &Lischinski 2001

7/ Adaptive Computing and Networking Lab, CSIE, NCU Visualization streaming Large volume Time-varying Dedicated servers Olbrich & Pralle 1999

8/ Adaptive Computing and Networking Lab, CSIE, NCU Image-based streaming Server- rendered Thin clients Less responsive Cohen-Or et. al. 2002

9/ Adaptive Computing and Networking Lab, CSIE, NCU Do we need 3D streaming? MMOGs Next-generation consoles (PS3, XBox360) Earth-scale virtual environment

10/ Adaptive Computing and Networking Lab, CSIE, NCU The BIG question How can 3D streaming be realized for a virtual environment with millions of concurrent users? The obvious problems  Large contents size (bandwidth)  Visibility calculations(CPU power) Everybody is watching a different movie!

11/ Adaptive Computing and Networking Lab, CSIE, NCU P2P-based 3D Scene Streaming Models & assumptions  Many 3D objects(position, orientation)  User navigations(AOI visibility)  Objects are fragmented (base & refinement pieces)  Initially stored at server

12/ Adaptive Computing and Networking Lab, CSIE, NCU Requirements User's perspective  Visual quality (fill ratio)  Interactivity (base & completion latency) Server's perspective  Requests can be redirected (save bandwidth)  Visibility calculation is distributed (save CPU)

13/ Adaptive Computing and Networking Lab, CSIE, NCU Challenges Distributed visibility determination  Global knowledge should not be needed  Scene partition & distribution required Peer and piece selection  Availability, peer capacities, network conditions  Roughly sequential transfer

14/ Adaptive Computing and Networking Lab, CSIE, NCU Conceptual framework Partition(for scene) Fragmentation(progressive mesh & texture) Prefetching(behavior-based) Prioritization(visibility determination) Selection(peer & piece selection)

15/ Adaptive Computing and Networking Lab, CSIE, NCU 3D streaming processes (client)

16/ Adaptive Computing and Networking Lab, CSIE, NCU Design of FLoD Users have shared visibility (contents from peers) Assume P2P-VE overlay Basic design  Each object has ID & location point  Scene description records orientation & scale  World is partitioned into cells

17/ Adaptive Computing and Networking Lab, CSIE, NCU

18/ Adaptive Computing and Networking Lab, CSIE, NCU Interface between FLoD & App

19/ Adaptive Computing and Networking Lab, CSIE, NCU Procedures Login Obtain scene descriptions (cell list) Obtain objects (request list) Request for piece (peer & piece selection) Move Logout

20/ Adaptive Computing and Networking Lab, CSIE, NCU Policies Content discovery(query-based) Peer selection(random) Piece selection(sequential) Server request condition (nearest, within dist) Concurrent transmission(limit to 4) Caching(5 x AOI)

21/ Adaptive Computing and Networking Lab, CSIE, NCU Prototype Implementation

22/ Adaptive Computing and Networking Lab, CSIE, NCU Partition Cell-based construction Use an actual game scene 100x game scene (514KB -> 51.8MB)

23/ Adaptive Computing and Networking Lab, CSIE, NCU Fragmentation

24/ Adaptive Computing and Networking Lab, CSIE, NCU Prioritization Visual importance

25/ Adaptive Computing and Networking Lab, CSIE, NCU Piece request list

26/ Adaptive Computing and Networking Lab, CSIE, NCU Selection Query Random request Ask server if none of the peers responded

27/ Adaptive Computing and Networking Lab, CSIE, NCU LAN Experiment 8 people, 10 Mbps LAN 40 min. 34 traces

28/ Adaptive Computing and Networking Lab, CSIE, NCU Simulation Evaluation Simulation methods  Choose VON as the P2P-NVE overlay  1000 x 1000 world, 100x100 cell  Randomly generated objects (500 total, 5 / cell) 15 kb (3kb base piece, 1.2 refinements)  Bandwidth limitation: Server:10 Mbps / 10 Mbps Clients: 1 Mbps / 512 Kbps  100ms/step, 3000 steps

29/ Adaptive Computing and Networking Lab, CSIE, NCU Simulation Results Scalability  Bandwidth use(kb / sec)  clients & server Streaming Quality  Fill ratio(%)  Base latency(sec)  Peer hit ratio (%)

30/ Adaptive Computing and Networking Lab, CSIE, NCU Server upload time-series (400 nodes)

31/ Adaptive Computing and Networking Lab, CSIE, NCU Server upload

32/ Adaptive Computing and Networking Lab, CSIE, NCU Client upload/download

33/ Adaptive Computing and Networking Lab, CSIE, NCU Fill ratio

34/ Adaptive Computing and Networking Lab, CSIE, NCU Base latency

35/ Adaptive Computing and Networking Lab, CSIE, NCU Hit ratio

36/ Adaptive Computing and Networking Lab, CSIE, NCU Effects of node density

37/ Adaptive Computing and Networking Lab, CSIE, NCU Effects of data density

38/ Adaptive Computing and Networking Lab, CSIE, NCU Discussions Distributed visibility determination  Pre-partitioning to cells  Obtainment of scene descriptions Peer & piece selection  Multiple data sources via AOI neighbors  Fault-tolerant to node failures

39/ Adaptive Computing and Networking Lab, CSIE, NCU Conclusion Peer-to-peer is a promising way for 3D streaming Neighbor discovery from P2P-NVE helps  Distributed visibility determination  Peer & piece selection An important area to both graphics and networking

40/ Adaptive Computing and Networking Lab, CSIE, NCU Future Work Data retrieval from non-AOI nodes Piece dependency considerations Prefetching & caching