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Rate Distortion Optimization for Mesh-based P2P Video Streaming PowerPoint PPT Presentation

Rate Distortion Optimization for Mesh-based P2P Video Streaming Tareq Hossain, Yi Cui, Yuan Xue V anderbilt A dvanced Net work and S ystems Group Vanderbilt University, USA Presenter: Dr. Sachin Agarwal Deutsche Telekom Laboratories Outline Motivation Video Broadcast Can P2P Help?

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Rate Distortion Optimization for Mesh-based P2P Video Streaming

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Rate distortion optimization for mesh based p2p video streaming l.jpg

Rate Distortion Optimization for Mesh-based P2P Video Streaming

Tareq Hossain, Yi Cui, Yuan Xue

Vanderbilt Advanced Network and Systems Group

Vanderbilt University, USA

Presenter: Dr. Sachin Agarwal

Deutsche Telekom Laboratories


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Outline

  • Motivation

    • Video Broadcast

    • Can P2P Help?

    • Rate Distortion for P2P Mesh

  • Rate Optimization

  • Simulation

  • Results

  • Conclusion


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Motivation

  • Video Broadcast

    • Increasing popularity due to wide use of internet

  • Can P2P Help?

    • Cost effective resource utilization

      • CPU cycles

      • Storage space

      • Uplink bandwidth

    • Instant deployability

      • Almost ubiquitous network coverage in the absence of CDN services and IP multicast


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Rate Distortion for P2P Mesh

  • Mesh based P2P can fully utilize the network resources of its peers compared to a tree based network

  • We use distributed algorithm – each peer adjusts its own streaming rate to reach the global optimum by satisfying:

    • Capacity constraint

    • Relay constraint

  • Double Pricing Solution

    • Simultaneous incorporation of capacity constraint and relay constraint significantly reduces the aggregate rate distortion

  • Single Pricing Solution

    • Relay constraint is applied after rate distortion algorithm converges

  • We present rate-distortion optimization for P2P mesh network

    • Double pricing solution performs better than single pricing solution


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Outline

  • Motivation

  • Rate Optimization

    • Performance Evaluation

    • Problem Formulation

    • Distributed Algorithm

  • Simulation

  • Results

  • Conclusion


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Performance Evaluation

  • Video quality is measured as the Mean-Square-Error (MSE) averaged over all frames

  • PSNR is used to quantify video quality, defined by

    • D represents the overall Mean-Square-Error (MSE) averaged over all frames of an encoded video sequence

  • The distortion D as a function of streaming rate xf is given by

    • The variables (θ, x0 and D0 ) depend on encoded video sequence as well as on the percentage of intra coded macroblocks.


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Problem Formulation

Rate optimization is a convex function of the allocated rate

Here f represents a flow between two peers, x is the rate vector and c is the capacity vector

A is an L x F (link, flow) matrix of links and flows such that Alf = 1 if flow f goes through link l and 0 otherwise

B is an F x F sparse matrix, where ((hk – 1)H + hi)th row is active only if there is a flow from peer hk to peer hi. Formally,


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Distributed Algorithm

  • Each receiving peer ( ) calculates the rates of its incoming flows in a mesh

    • Network price:

    • Net relay price:

    • Source Rate update for each peer:

    • Rate is updated based on the minimum of network and net relay price available among the all incoming flows

  • Rate update for incoming flows:

  • Rate update for incoming flows with minimum network and net relay price:

link price

relay price


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Outline

  • Motivation

  • Rate Optimization

  • Simulation

    • Configuration

    • Input Data

    • Multicast Tree Construction

  • Results

  • Conclusion


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Configuration

  • To determine the actual allocated rate, we choose the highest quantized rate that is immediately less than the rate achieved by our solution

  • The ITU-T test sequences used are: foreman, akiyo, hall, mother-daughter

  • The server has a fixed rate of 2Mbps

  • The maximum number of peers ~160

  • The uplink bandwidth of each peer is randomly assigned between 0.6Mbps and 2Mbps


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Input data

  • The PSNR-Rate video input data (a) and Number of peers-Time data (b):

Rate (Kbps)


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Multicast Mesh Construction

  • Peers join the streaming network one-by-one

    • Joining peer uses the spare capacity of existing peers to determine a suitable parent. The spare coefficient is defined as

    • Here xf(h)is the incoming flow rate of the peer h

  • Implementation

    • At the end of each rate update cycle, peers send their spare coefficient value to parents

    • The ID of the best suitable parent propagates to the server


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Outline

Motivation

Rate Optimization

Simulation

Results

Conclusion


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Results

  • The average PSNR gain over all the videos for the double pricing solution is 1.86 dB (PSNR is 0 when all peers leave ~720s)


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Results

  • The average gain for the double pricing solution represented in terms of rate


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Conclusion

We present an optimal rate allocation solution for P2P mesh network

We use non-linear optimization framework

Minimize aggregate distortion

Maximize the overall PSNR among all peers in a P2P mesh

Simultaneously apply peer relaying constraint along with capacity constraint

Double pricing solution consistently performs better than single pricing solution


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Thank You

VANETS (Vanderbilt Advanced Network and Systems) Group

http://vanets.vuse.vanderbilt.edu

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