Peer-to-Peer 3D Streaming ACM Multimedia 2007 submission Presenter: Shun-Yun Hu ( 胡舜元 ) [email protected] Adaptive Computing and Network Lab Dept. of CSIE,

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

2/

Adaptive Computing and Networking Lab, CSIE, NCU

Outline

Introduction P2P-based 3D Scene Streaming Design of FLoD Prototype Implementation Simulation Evaluation Conclusion

3/


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/


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/


Object streaming

Hoppe 1996Progressive Meshes

6/


Scene streaming Many objects Remote walk-

through Object

selections & transmissions

Teler &Lischinski2001

7/


Visualization streaming Large volume Time-varying Dedicated

servers

Olbrich & Pralle 1999

8/


Image-based streaming

Server-rendered

Thin clients Less

responsive

Cohen-Or et. al.2002

9/


Do we need 3D streaming?

MMOGs Next-generation consoles (PS3, XBox360)

Earth-scale virtual environment

10/


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/


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/


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/


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/


Conceptual framework

Partition (for scene) Fragmentation (progressive mesh & texture) Prefetching (behavior-based) Prioritization (visibility determination) Selection (peer & piece selection)

15/


3D streaming processes (client)

16/


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

17/


18/


Interface between FLoD & App

19/


Procedures

Login Obtain scene descriptions (cell list) Obtain objects (request list) Request for piece (peer & piece

selection) Move Logout

20/


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/


Prototype Implementation

22/


Partition

Cell-based construction Use an actual game scene 100x game scene (514KB -> 51.8MB)

23/


Fragmentation

24/


Prioritization

Visual importance

25/


Piece request list

26/


Selection

Query Random request Ask server if none of the peers responded

27/


LAN Experiment

8 people, 10 Mbps LAN 40 min. 34 traces

28/


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/


Simulation Results

Scalability Bandwidth use (kb / sec) clients & server

Streaming Quality Fill ratio (%) Base latency (sec) Peer hit ratio (%)

30/


Server upload time-series (400 nodes)

31/


Server upload

32/


Client upload/download

33/


Fill ratio

34/


Base latency

35/


Hit ratio

36/


Effects of node density

37/


Effects of data density

38/


Discussions

Distributed visibility determinationPre-partitioning to cellsObtainment of scene descriptions

Peer & piece selectionMultiple data sources via AOI neighborsFault-tolerant to node failures

39/


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/


Future Work

Data retrieval from non-AOI nodes Piece dependency considerations Prefetching & caching

Documents

Peer-to-Peer 3D Streaming ACM Multimedia 2007 submission Presenter: Shun-Yun Hu ( 胡舜元 ) [email protected] Adaptive Computing and Network Lab Dept. of CSIE,