Automatic construction of personalized TV news programs
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
An integrated scheme for object-based video abstraction
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Heuristic Solutions for the Multiple-Choice Multi-dimension Knapsack Problem
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Design and Implementation of a Caching System for Streaming Media over the Internet
RTAS '00 Proceedings of the Sixth IEEE Real Time Technology and Applications Symposium (RTAS 2000)
A Novel Cache Scheme for Cluster-Based Streaming Proxy Server
ICDCSW '05 Proceedings of the Seventh International Workshop on Multimedia Network Systems and Applications - Volume 07
Motion Panoramas: Research Articles
Computer Animation and Virtual Worlds
Parallel Algorithms for Motion Panorama Construction
ICPPW '06 Proceedings of the 2006 International Conference Workshops on Parallel Processing
Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Client-centered multimedia content adaptation
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video personalization and summarization system for usage environment
Journal of Visual Communication and Image Representation
A New Heuristic for Solving the Multichoice Multidimensional Knapsack Problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Efficient summarization of stereoscopic video sequences
IEEE Transactions on Circuits and Systems for Video Technology
Personalized content adaptation using multimodal highlights of soccer video
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Proceedings of the 3rd Multimedia Systems Conference
Personalized sports video customization for mobile devices
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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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.