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Joint bandwidth allocation, data scheduling and incentives for scalable video streaming over peer-to-peer networks

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Abstract

The overall performance of a peer-to-peer (P2P) scalable streaming system largely depends on the strategies employed in bandwidth allocation, data scheduling and incentives. In this paper, we develop a credit-based content-aware bandwidth auction model for scalable streaming in P2P networks. It formulates multi-overlay multi-layer bandwidth request and allocation problems as auction games. Each peer in the games acts as both auctioneer and player. Being a auctioneer, it maximizes the total revenue (credits) by selling upload bandwidth; Being a player, it uses the credits earned in bandwidth sales to sequentially bid for layer bandwidth so as to maximize the received video quality. Also, a content-aware bidding strategy is proposed, under which the required bandwidth quantity from a peer is determined by the informative video chunks and the marginal net utility that peer could provide, as well as the available credits and the maximum layer bit rate. The convergence of the proposed auction algorithm is mathematically proved. Finally, the performance of the proposed scheme is verified by simulation results.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California, San Diego, CA, 92093, USA

    Junni Zou

  2. Key Laboratory of Special Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, 200072, China

    Junni Zou

  3. Department of Communication and Information Engineering, Shanghai University, Shanghai, 200072, China

    Lin Chen

Authors
  1. Junni Zou
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  2. Lin Chen
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Correspondence to Junni Zou.

Additional information

The work has been partially supported by the NSFC grants (No. 61271211), and the Research Program from Shanghai Science and Technology Commission (No. 11510707000, No. 11QA1402600.

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Zou, J., Chen, L. Joint bandwidth allocation, data scheduling and incentives for scalable video streaming over peer-to-peer networks. Multimed Tools Appl 73, 1269–1289 (2014). https://doi.org/10.1007/s11042-013-1621-7

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  • DOI: https://doi.org/10.1007/s11042-013-1621-7

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