Collaborative caching for efficient dissemination of personalized video streams in resource constrained environments

The ever increasing deployment of broadband networks and simultaneous proliferation of low-cost video capturing and multimedia-enabled mobile devices such as smart cellular phones, netbook computers, and tablet computers have triggered a wave of novel mobile multimedia applications making video streaming on mobile devices increasingly popular and commonplace. Networked environments consisting of mobile devices tend to be highly heterogeneous in terms of client-side and system-wide resource constraints, clients' queries for information, geospatial distribution, and dynamic trajectories of the mobile clients, and client-side and server-side privacy and security requirements. Invariably, the video streams need to be personalized to provide a resource-constrained mobile device with video content that is most relevant to the client's request while simultaneously satisfying the client-side and system-wide resource constraints, privacy and security requirements and the constraints imposed by the geospatial distribution and dynamic trajectories of the mobile clients relative to the server(s). 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 Latency-Biased Collaborative Caching (LBCC) 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 among 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.