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Shun-Yun Hu Department of Computer Science and Information Engineering National Central University Dissertation Advisor: Prof. Jehn-Ruey Jiang 2009/11/17. Peer-to-Peer 3D Streaming Dissertation Oral Exam. Motivation. Two trends in virtual environments (VEs)

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peer to peer 3d streaming dissertation oral exam

Shun-Yun Hu

Department of Computer Science and Information Engineering

National Central University

Dissertation Advisor: Prof. Jehn-Ruey Jiang

2009/11/17

Peer-to-Peer 3D StreamingDissertation Oral Exam
motivation
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?

outline
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
what is 3d streaming
Continuous and real-time delivery of 3D content

over network connections to allow

user interactions without a full download.

What is 3D streaming?
object streaming
Hoppe 1996

Progressive Meshes

Object streaming
scene streaming
Multiple objects

Object selection & transmission

Teler &Lischinski

2001

Scene streaming
visualization streaming
Large volume

Time-varying

Resource intensive

Olbrich & Pralle

1999

Visualization streaming
image based streaming
Server-rendered

Thin clients

Less responsive

Cohen-Or et. al.

2002

Image-based streaming
3d streaming vs media streaming
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
outline21
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
model and assumptions
For a given object (mesh or texture)

All content is initially stored at a server

Model and assumptions
state vs content management
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
3d streaming requirements
Streaming quality

User's perspective

“how much?” & “how fast?”

Speed

Scalability

Server's perspective

How to offload?

Concurrent users

3D streaming requirements
challenges for p2p 3d streaming
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
a conceptual model
A conceptual model
  • Pre-install: movement, rendering (client)
  • 3D streaming: partition + fragmentation (server)

prefetching + prioritization (client)

  • P2P: selection (client)
p2p 3d streaming issues
P2P 3D streaming issues
  • Object discovery
  • Source discovery
  • State exchange
  • Content exchange

P2P video/file sharing

outline29
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
observation
Observation
  • Users tend to cluster at hotspots
  • Overlapped visibility = shared content
object discovery via scene descriptions
Object discovery via scene descriptions

star: self triangles: neighbors

circle: AOI rectangles: objects

source neighbor discovery via von
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]

flowing level of details flod
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

system architecture
System architecture
  • Data flows

(A): scene request list (B): scene descriptions

(C): piece request list (D): object pieces

prototype experiment
Prototype experiment
  • Progressive models in a scene (by NTU)
  • Peer-to-peer AOI neighbor requests (by NCU)
prototype experiment36
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%
simulation setup
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)‏
simulation metrics
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)
outline47
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
problems with basic flod
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
flod enhancements
FLoD enhancements
  • Enhanced peer & piece selection
    • Wei-Lun Sung (ACM NOSSDAV’08)
  • Bandwidth-aware streaming
    • Chien-Hao Chien (ACM NetGames’09)
enhanced selection
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

‧‧‧‧

50/

multi level aoi request
Multi-Level AOI Request
  • Localized requests may prevent contentions
  • Peers request from closer neighbors/levels first

51/

simulation environment
Simulation Environment
  • Compare enhanced strategy with FLoD
bandwidth aware peer selection
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
simulation environment56
Simulation environment

[Bharambe et al, 2006]

streaming quality bw utilization
Streaming quality (= BW utilization)
  • 100 to 500 objects, fixed at 100 peers
system scalability
System scalability
  • 50 to 450 peers, fixed 300 objects
fill ratio time series qos
Fill ratio time-series (QoS)

original FLoD Enhanced

outline60
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
loddt cavagna et al 2006

LODDT(Cavagna et al. 2006)

Object

Tree Node

Aura

U

hyperverse botev et al 2008
HyperVerse (Botev et al, 2008)
  • Backbone + overlay architecture
outline64
Outline
  • Introduction
  • Background
  • A Model for P2P 3D Streaming
  • The Design and Evaluation of FLoD
  • FLoD Extensions
  • Discussions
  • Conclusion
summary
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
  • FLoD’s performance can be enhanced with
    • Pushed-based state exchange
    • Pre-allocated fixed-size bandwidth channels
conclusion
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
future works
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
flod publications
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.
slide69
Q & A

Thank you!

http://ascend.sourceforge.net

related work
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]
secure p2p 3d streaming
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)
extended candidate buffer
Extended Candidate Buffer
  • Non-AOI neighbors may still possess data
  • Maintain extra list of non-AOI neighbors

S

R

Obj

74/