A window-assisted video partitioning strategy for partitioning and caching video streams in distributed multimedia systems

  • Authors:

  • Xiaorong Li;Bharadwaj Veeravalli;Viktor K. Prasanna

  • Affiliations:

  • Department of Advanced Computing, Institute of High Performance Computing, 1 Science Park Road, #01-10 The Capricorn, Singapore 117528, Singapore;Computer Networks and Distributed Systems (CNDS) Laboratory, Department of Electrical and Computer Engineering, The National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Sing ...;Ming Hsieh Department of Electrical Engineering, University of Southern California, LA, USA

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2007

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Abstract

In this paper, we address the issue of efficiently streaming a set of heterogenous videos under the constraint of service latency over a scalable multimedia systems. We propose a novel strategy, referred to as window-assisted video partitioning (WAVP), for rendering cost-effective multimedia services. The objective is to minimize the service cost and maximize the number of requests that can be successfully served under resources constraints (cache capacity and link bandwidth). We formulate the problem of video partitioning as an optimization of both bandwidth resources and cache space, and derive the optimal schedule window for different video portions under consideration of time constraints, the popularities and the sizes of the video portions. In WAVP, video are partitioned into multiple portions and delivered according to by adaptive schedule windows. We prove that WAVP strategy not only optimize the service cost but also be able to serve requests under the time constraints without causing too much delay. We conduct mathematical analysis and derive certain performance bounds that quantify the overall performance of the strategy. It shows that the service cost can be optimized by adjusting the schedule window and resources utilization can be improved as video streams are partitioned into multiple portions. We evaluate the performance under several influencing parameters such as available bandwidth, cache capacity, and partition gradients. Simulation results show that our proposed method can not only significantly reduce the service cost under tight time constraints and with low partition overhead, but also balance the utilization of network resources to achieve high acceptance ratio with low average service cost.