Optimized Distributed Delivery of Continuous-Media Documents over Unreliable Communication Links

  • Authors:

  • Gerassimos Barlas;Bharadwaj Veeravalli

  • Affiliations:

  • IEEE Computer Society;IEEE

  • Venue:
  • IEEE Transactions on Parallel and Distributed Systems
  • Year:
  • 2005

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Abstract

Video-on-Demand (VoD) applications place very high requirements on the delivery medium. High-quality services should provide for a timely delivery of the data-stream to the clients plus a minimum of playback disturbances. The major contributions of this paper are that it proposes a multiserver, multi-installment (MSMI) solution approach (sending the document in several installments from each server) to the delivery problem and achieves a minimization of the client waiting time, also referred to as the access time (AT) or start-up latency in the literature. By using multiple spatially distributed servers, we are able to exploit slow connections that would otherwise prevent the deployment of Video-On-Demand-like services, to offer such services in an optimal manner. Additionally, the delivery and playback schedule that is computed by our approach is loss-aware in the sense that it is flexible enough to accommodate packet losses without interrupts. The mathematical framework presented covers both computation and optimization problems associated with the delivery schedule, offering a complete set of guidelines for designing MSMI VoD services. The optimizations presented include the ordering of the servers and determining the number of installments based on the packet-loss probabilities of the communication links. Our analysis guarantees the validity of a delivery schedule recommended by the system by providing a percentage of confidence for an uninterrupted playback at the client site. This, in a way, quantifies the degree of quality of service rendered by the system and the MSMI strategy proposed. The paper is concluded by a rigorous simulation study that showcases the substantial advantages of the proposed approach and explores how optimization of the schedule parameters affects performance.