Video personalization in resource-constrained multimedia environments

Multimedia data, especially video data, is being increasingly transmitted to, transmitted from and viewed on mobile devices such as PDA's, laptop PCs, pocket PCs and cell phones. One of the natural limitations of these multimedia-capable, mobile devices is that they are constrained by their battery power capacity, viewing time limit, amount of data received, and in many situations, by available network bandwidth connecting these devices with video servers. The video server is typically also constrained by its computing power and connection bandwidth. In order to provide a resource-constrained mobile client with its desired video content, it is necessary to adapt or personalize the video content while simultaneously satisfying the aforementioned constraints. Also, in order to limit the client-experienced latency, it is necessary to perform client request aggregation on the server end. To this end, a video personalization strategy is proposed to provide mobile, resource-constrained clients with personalized video content that is most relevant to the client's request while simultaneously satisfying multiple client-side system-level resource constraints. A client request aggregation strategy is also proposed to cluster client requests with similar video content preferences and similar client-side resource constraints such that the number of requests the server needs to process and the client-experienced latency are both reduced.

The primary contributions of the paper are (1) the formulation and implementation of a Multiple-choice Multi-dimensional Knapsack Problem (MMKP)-based video personalization strategy; and (2) the design and implementation of a multi-stage clustering-based client request aggregation strategy. Experimental results comparing the proposed MMKP-based video personalization strategy to existing 0/1 Knapsack Problem (0/1KP)-based and the Fractional Knapsack Problem (FKP)-based video personalization strategies are presented. It is observed that (1) the proposed MMKP-based personalization strategy includes more relevant video content in response to the client's request compared to the existing 0/1KP-based and FKP-based personalization strategies; and (2) in contrast to the 0/1KP-based and FKP-based personalization strategies which can satisfy only a single client-side constraint at a time, the proposed MMKP-based personalization strategy is shown to be capable of satisfying simultaneously multiple client-side resource constraints. Experimental results comparing the client-experienced latency with and without the proposed client request aggregation strategy are also presented. It is shown that the proposed client request aggregation strategy significantly reduces the mean client-experienced latency without significant reduction in the average relevance value of the video content delivered in response to the client's request.