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Selection Strategies for Peer-to-Peer 3D Streaming

Wei-Lun Sung, Shun-Yun Hu, Jehn-Ruey Jiang

National Central University, Taiwan

2008/05/29

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Virtual environments (VE) VEs allow users to interact in synthetic worlds Larger content & more worlds content streaming (i.

e., 3D streaming) becomes necessary

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3D streaming Continuous and real-time delivery of 3D content to

allow user interactions without a full download. Object streaming fragments mesh into base & refinements

Base 1 2 3Refinements

User

(Hoppe 96)

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Scene streaming multiple objects object selection & prioritization

[Teler &

Lischinski 2001]

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Comparison with media streaming

Highly interactive (latency-sensitive) Behavior-based (non-linear)

How to scale to 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 a city

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and a building

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Right now it’s flat…

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But in the near future…

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Observation Limited & predictable area of interest (AOI) Overlapped visibility = shared content

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Benefits of peer-to-peer Scalable

Growing amount of total resources

Affordable Commodity PCs

Feasible Better client hardware (CPU, broadband networks) Availability of user-hosted machines

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Peer selection Choose suitable candidates so that content

retrieval can be done quickly and efficiently

Source discoveryWhich peers possess the needed data

Source selectionWhich peers to request the data

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Related Work: FLoD [Infocom 2008]

VE partitioned into cells with scene descriptions Assumes P2P overlay that provides AOI neighbors

star: self triangles: neighborscircle: AOI rectangles: objects

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Peer selection in FLoD Source discovery

Query-responseExtra delay due to queries

Source selectionRandom selectionRequests contention due to overlapping requests

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OBJ

Request contention problem

Overlapping requests create contentions

R1

R2R3

R4

R5

R6

R1,R2

R1,R2,R3

R1,R2,R3,R4,R5,R6

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Proposed Solutions

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Incremental Piece List Exchange Proactive notification of content availability Periodic incremental exchange of content

availability information with neighbors.

Msg_Type Obj_ID Max_PID Obj_ID Max_PID ‧‧‧‧

incremental content information

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Extended Candidate Buffer Non-AOI neighbors may still possess data Maintain extra list of non-AOI neighbors

RS Obj

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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 Based on FLoD (available on SourceForge)

World size: 1000 x 1000 Simulation steps: 3000 Objects: 500 Nodes: 50 ~ 500 (50 nodes increase) AOI radius: 75

Server bandwidth: 10 Mbps / 10 Mbps Peer bandwidth: 1 Mbps / 256 Kbps

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Simulation Environments (cont.) Source discovery

(QR) query-response: 5 steps interval, 10 requests (EE) exchanged & extended: 150 radius

Source selection (RAND) random (ML) multi-level AOI request : 4 levels

Original FLoD: QR-RAND Proposed method: EE-ML

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Hit Ratio

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Base Latency

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Fill ratio

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Bandwidth (Server)

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Bandwidth (Clients source discovery)

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Conclusion New selection strategies for P2P 3D streaming

Availability info exchange & extended candidate buffer reduce both latency and bandwidth overhead

multi-level AOI requests obtain data from closer providers but improve only hit ratio

Future work More sources Physical topology Pre-fetching

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Q & A

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Neighbor discovery via VON

Boundary neighbors

New neighbors

Non-overlapped neighbors

[Hu et al. 06]

Voronoi diagrams identify boundary neighbors for neighbor discovery

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LODDT

‧ ‧‧

‧‧

Object Tree Node Aura

U

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LODDT (cont.) Discovery

Estimation Selection

Every peer samples the time-to-serve (TTS) of its neighbors

Requestors organize their data requests so as obtain tree nodes in the right order

Drawback: incorrect estimation, congestion

Requests Candidates

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Simulation Environments (cont.) System performance

Hit ratio: Ratio of successful requests peers have sent Latency: Duration between initial request and data arrival Fill ratio: Ratio of the possessed required data

Scalability metrics Bandwidth usage (consumption) Content discovery overhead