peer-to-peer 3d streaming acm multimedia 2007 submission presenter: shun-yun hu ( 胡舜元 )...
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Peer-to-Peer 3D Streaming
ACM Multimedia 2007submission
Presenter: Shun-Yun Hu (胡舜元 )[email protected]
Adaptive Computing and Network LabDept. of CSIE, National Central University
2007/04/17
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Adaptive Computing and Networking Lab, CSIE, NCU
Outline
Introduction P2P-based 3D Scene Streaming Design of FLoD Prototype Implementation Simulation Evaluation Conclusion
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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
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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
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Adaptive Computing and Networking Lab, CSIE, NCU
Scene streaming Many objects Remote walk-
through Object
selections & transmissions
Teler &Lischinski2001
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Adaptive Computing and Networking Lab, CSIE, NCU
Visualization streaming Large volume Time-varying Dedicated
servers
Olbrich & Pralle 1999
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Adaptive Computing and Networking Lab, CSIE, NCU
Image-based streaming
Server-rendered
Thin clients Less
responsive
Cohen-Or et. al.2002
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Adaptive Computing and Networking Lab, CSIE, NCU
Do we need 3D streaming?
MMOGs Next-generation consoles (PS3, XBox360)
Earth-scale virtual environment
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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!
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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
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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)
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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
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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)
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Adaptive Computing and Networking Lab, CSIE, NCU
Design of FLoD
Users have shared visibility (contents from peers) Assume P2P-VE overlay
Basic designEach object has ID & location pointScene description records orientation & scaleWorld is partitioned into cells
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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
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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)
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Adaptive Computing and Networking Lab, CSIE, NCU
Partition
Cell-based construction Use an actual game scene 100x game scene (514KB -> 51.8MB)
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Adaptive Computing and Networking Lab, CSIE, NCU
Selection
Query Random request Ask server if none of the peers responded
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Adaptive Computing and Networking Lab, CSIE, NCU
LAN Experiment
8 people, 10 Mbps LAN 40 min. 34 traces
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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
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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 (%)
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Adaptive Computing and Networking Lab, CSIE, NCU
Discussions
Distributed visibility determinationPre-partitioning to cellsObtainment of scene descriptions
Peer & piece selectionMultiple data sources via AOI neighborsFault-tolerant to node failures
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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