Peer-to-Peer 3D Streaming Dissertation Oral Exam

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Peer-to-Peer 3D StreamingDissertation Oral Exam

• Shun-Yun Hu
• Department of Computer Science and Information
  Engineering
• National Central University
• Dissertation Advisor Prof. Jehn-Ruey Jiang
• 2009/11/17

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Motivation

• Two trends in virtual environments (VEs)
• Larger and more dynamic content
• More worlds
• Content streaming is needed
• 80 - 90 content is 3D (e.g., 3D streaming)
• How to support millions of concurrent users?

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Imagine you start with a globe
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Zoom in
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To Chung-Li
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and NCU
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Right now its flat
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But in the near future
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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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What is 3D streaming?

• Continuous and real-time delivery of 3D content
• over network connections to allow
• user interactions without a full download.

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Object streaming

• Hoppe 1996
• Progressive Meshes

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Scene streaming

• Multiple objects
• Object selection transmission
• Teler Lischinski
• 2001

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Visualization streaming

• Large volume
• Time-varying
• Resource intensive
• Olbrich Pralle
• 1999

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Image-based streaming

• Server-rendered
• Thin clients
• Less responsive
• Cohen-Or et. al.
• 2002

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3D streaming vs. media streaming

• Video / audio media streaming is very matured
• User access patterns are different for 3D content
• Highly interactive ? Latency-sensitive
• Behaviour-dependent ? Non-sequential
• Analogy
• Constant frequent switching of multiple channels

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The scalability problem

• Client-server has inherent resource limit

Resource limit
Funkhouser95
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A potential solution

• Peer-to-Peer Use the clients resources

Resource limit
Keller Simon 2003
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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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World model area of interest (AOI)
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Model and assumptions

• For a given object (mesh or texture)
• All content is initially stored at a server

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State vs. content management

• State management
• Small updatable ( KB)
• May require security / anti-cheating
• Ex. Avatar positions, health points, equipments
• Content management
• Large relatively static ( MB)
• May authenticate via hashing
• Ex. 3D polygonal models textures

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3D streaming requirements

• Streaming quality
• User's perspective
• how much? how fast?
• Speed
• Scalability
• Server's perspective
• How to offload?
• Concurrent users

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Challenges for P2P 3D streaming

• Distributed visibility determination
• Minimize server involvement
• Efficient determination without global knowledge
• Dynamic group management
• Discovery of data sources
• Continuous avatar movements and real-time
  constrain
• Peer piece selection
• Optimal visual quality
• Content availability and bandwidth constrain

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A conceptual model

• Pre-install movement, rendering (client)
• 3D streaming partition fragmentation (serv
  er)
• prefetching prioritization (client)
• P2P selection (client)

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P2P 3D streaming issues

• Object discovery
• Source discovery
• State exchange
• Content exchange

P2P video/file sharing
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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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Observation

• Users tend to cluster at hotspots
• Overlapped visibility shared content

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Object discovery via scene descriptions

• star self triangles neighbors
• circle AOI rectangles objects

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Source (neighbor) discovery via VON
Voronoi diagrams identify boundary neighbors for
neighbor discovery
Non-overlapped neighbors
Boundary neighbors
New neighbors
Hu et al., IEEE Network, 2006
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Flowing Level-of-Details (FLoD)

• Object discovery scene descriptions
• Source discovery VON
• State exchange query-response (pull)
• Content exchange random peer selection
• sequential piece selection

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System architecture

• Data flows
• (A) scene request list (B) scene
  descriptions
• (C) piece request list (D) object pieces

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Prototype experiment

• Progressive models in a scene (by NTU)
• Peer-to-peer AOI neighbor requests (by NCU)

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Prototype experiment

• Data
• 3D scene from a game demo (total 50 MB)
• Setup
• 100 Mbps LAN
• 10 participants, 48 logins captured in 40 min.
• Results
• Found matching client upload download
• Avg. server request ratio (SRR) 36

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Simulation setup

• Environment
• 1000x1000 world, 100ms / step, 3000 steps
• client 1 Mbps / 256 Kbps, server 10 Mbps
  (both)?
• Objects
• Random object placement (500 objects)?
• Object size based on prototype ( 15 KB / object)
• User behavior
• Random clustering movement (1.5 ln(n)
  hotspots)?

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Simulation metrics

• Scalability
• Bandwidth usage (Kbytes / sec)
• Server request ratio ( obtained from server)
• Streaming quality
• Base latency (delay to obtain 1st piece)
• Fill ratio (obtained / visible data)

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Server bandwidth usage
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Client bandwidth usage (random)
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Client bandwidth usage (cluster)?
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Effect of user density
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Fill ratio
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Base latency
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Effect of upload bandwidth
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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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Problems with basic FLoD

• Source discovery too few sources
• State exchange pull may be slow
• Content exchange better than random?
• Real environment considerations
• Peer heterogeneity
• Bandwidth utilization

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FLoD enhancements

• Enhanced peer piece selection
• Wei-Lun Sung (ACM NOSSDAV08)
• Bandwidth-aware streaming
• Chien-Hao Chien (ACM NetGames09)

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Enhanced Selection

• Proactive notification of availability (push)
• Periodic incremental exchange of content
  availability information with neighbors.

incremental content information
Msg_Type
Obj_ID
Max_PID
Obj_ID
Max_PID
????
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Multi-Level AOI Request

• Localized requests may prevent contentions
• Peers request from closer neighbors/levels first

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Simulation Environment

• Compare enhanced strategy with FLoD

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Base Latency
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Fill ratio
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Bandwidth-aware Peer Selection

• Region-based Peer List to increase sources
• Pre-allocation of connection channels
• Multi-source peer selection
• Channel neighbors (bandwidth reservation)
• AOI neighbors (no response guarantee)
• Server (no response guarantee)
• Tit-for-Tat peer selection (from BitTorrent)
• Channel-neighbor first
• Higher contributor first

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Simulation environment
World Size 1000 x 1000 (units)
Cell Size 100 x 100 (units)
AOI Radius 100 (units)
Time steps 1500 (steps/ sec)
Object Data Size Range 100 300 (KB)
of Base Piece 10
Refinement Piece Size 5 (KB)
Server Bandwidth Download/Upload 1000/ 1000 (KB/sec)
User Bandwidth Distribution User Bandwidth Distribution User Bandwidth Distribution
Downlink (KB/sec) Uplink (KB/sec) Fraction of nodes
96 10 0.05
187 30 0.45
375 100 0.40
1250 625 0.10
Bharambe et al, 2006
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Streaming quality ( BW utilization)

• 100 to 500 objects, fixed at 100 peers

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System scalability

• 50 to 450 peers, fixed 300 objects

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Fill ratio time-series (QoS)

• original FLoD Enhanced

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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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LODDT (Cavagna et al. 2006)
Object
Tree Node
Aura
U
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HyperVerse (Botev et al, 2008)

• Backbone overlay architecture

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Comparisons
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Outline

• Introduction
• Background
• A Model for P2P 3D Streaming
• The Design and Evaluation of FLoD
• FLoD Extensions
• Discussions
• Conclusion

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Summary

• P2P 3D streaming has four main issues
• Object discovery
• Source discovery
• State exchange
• Content exchange
• FLoD demonstrates that P2P allows
• Much lower server resource usage
• Better performance in crowding
• FLoDs performance can be enhanced with
• Pushed-based state exchange
• Pre-allocated fixed-size bandwidth channels

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Conclusion

• 3D streaming could become an important net
  traffic
• Non-sequential access
• Latency-sensitive
• Peer-to-peer streaming is promising
• Reduce server resource usage
• Dynamic interest groups
• New area with many interesting issues
• Graphics progressive encoding / decoding,
  compression
• Networking group discovery, prefetching,
  topology, versioning

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Future works

• Practical Adoptions
• Dynamic content update
• Topology-aware P2P 3D streaming
• Secure P2P 3D streaming
• Open questions
• Many small worlds vs. one large world
• High-definition (HD) content
• Incentives killer apps

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FLoD publications

• Shun-Yun Hu, "A Case for 3D Streaming on
  Peer-to-Peer Networks," in Proc. ACM Web3D, Apr.
  2006, pp. 57-63.
• Shun-Yun Hu, Ting-Hao Huang, Shao-Chen Chang,
  Wei-Lun Sung, Jehn-Ruey Jiang, and Bing-Yu Chen,
  "FLoD A Framework for Peer-to-Peer 3D
  Streaming," in Proc. IEEE INFOCOM, pp. 1373-1381,
  Apr. 2008.
• Wei-Lun Sung, Shun-Yun Hu, and Jehn-Ruey Jiang,
  "Selection Strategies for Peer-to-Peer 3D
  Streaming," in Proc. NOSSDAV, May. 2008.
• Chang-Hua Wu, Shun-Yun Hu, and Li-Ming Tseng,
  "Discovery of Physical Neighbors for P2P 3D
  Streaming," in Proc. ICUMT, Oct. 2009.
• Mo-Che Chan, Shun-Yun Hu, and Jehn-Ruey Jiang,
  "Secure Peer-to-Peer 3D Streaming," Multimedia
  Tools and Applications, vol. 45, no. 1-3, Oct.
  2009, pp. 369-384.
• Chien-Hao Chien, Shun-Yun Hu, and Jehn-Ruey
  Jiang, "Bandwidth-Aware Peer-to-Peer 3D
  Streaming," in Proc. NetGames, Nov. 2009.
• Shun-Yun Hu, Jehn-Ruey Jiang, and Bing-Yu Chen,
  "Peer-to-Peer 3D Streaming," IEEE Internet
  Computing, to appear, 2009.

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Q A

• Thank you!
• http//ascend.sourceforge.net

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Related work

• 3D streaming
• Progressive meshes Hoppe 96
• Geometry image Gu et al. 02
• Scene streaming Teler and Lischinski 2001
• P2P media streaming
• Zigzag, oStream, Coolstreaming, Prime
• Nonlinear media streaming
• Channel Set Adaptation (CSA) Gotz, 2006
• P2P 3D streaming
• LOD-DT Cavagna et al. 2006

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Secure P2P 3D streaming

• How to authenticate content from untrusted peers?
• Four types of content
• Whole model (digital signature)
• Linear stream (hash chain)
• Independent stream (Rabin-based)
• Partially linear stream (hash DAG)

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Cache utilization
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Experimental results
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Extended Candidate Buffer

• Non-AOI neighbors may still possess data
• Maintain extra list of non-AOI neighbors

S
R
Obj
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