Collaborative caching for efficient dissemination of personalized video streams in resource constrained environments
Proceedings of the 3rd Multimedia Systems Conference on - MMSys '12
The ever increasing deployment of broadband networks and simultaneous proliferation of low cost video capturing and multimedia enabled mobile devices have triggered a wave of novel mobile multimedia applications, resulting in the development of large scale systems for delivery of video streams to heterogeneous resource constrained mobile clients. Invariably, the video streams need to be personalized to provide a resource constrained mobile device with video content that is most relevant to the
... st relevant to the client's request while simultaneously satisfying the client-side and system-wide resource constraints. In this paper we present the design and implementation of a distributed system, consisting of several geographically distributed video personalization servers and proxy caches, for efficient dissemination of personalized video in a resource constrained mobile environment. With the objective of optimizing cache performance, a novel cache replacement policy and multi-stage client request aggregation strategy, both of which are specifically tailored for personalized video content, are proposed. A novel latencybiased collaborative caching protocol based on counting Bloom filters is designed for further enhancing the scalability and efficiency of disseminating personalized video content. The benefits and costs associated with collaborative caching for disseminating personalized video content to resource constrained and geographically distributed clients are analyzed and experimentally verified. The impact of different levels of collaboration amongst the caches and, the advantages of using multiple video personalization servers with varying degrees of mirrored content on the efficiency of personalized video delivery, are also studied. Experimental results demonstrate that the proposed collaborative caching scheme, coupled with the proposed personalization-aware cache replacement and client request aggregation strategies, provides a means for efficient dissemination of personalized video streams in resource constrained environments.