SVC adaptation: Standard tools and supporting methods
Image Communication
A cross-layer framework for efficient streaming of H.264 video over IEEE 802.11 networks
Journal of Computer Systems, Networks, and Communications
Modeling of a QoS Matching and Optimization Function for Multimedia Services in the NGN
MMNS 2009 Proceedings of the 12th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services: Wired-Wireless Multimedia Networks and Services Management
IEEE Transactions on Image Processing
CDVE'09 Proceedings of the 6th international conference on Cooperative design, visualization, and engineering
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
Using Description Logics for the Provision of Context-Driven Content Adaptation Services
International Journal of Systems and Service-Oriented Engineering
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Many techniques exist for adapting videos to satisfy heterogeneous resource conditions or user preferences, whereas selection of the best adaptation operation among various choices usually is either ad hoc or inefficient. To provide a systematic solution, we present a conceptual framework based on utility function (UF), which models video entity, adaptation, resource, utility, and the relations among them. In order to support real-time video adaptation, we present a content-based statistical paradigm to facilitate the prediction of UF for real-time transcoding of live videos. Instead of modelling the UF through analytical models, as in the conventional rate-distortion framework, the proposed approach formulates the prediction as a classification and regression problem. Each video clip is classified into one of distinctive categories and then local regression is used to accurately predict the utility value. Our extensive experiment results based on MPEG-4 transcoding demonstrate that the proposed method achieves very promising performance - up to 89% accuracy in choosing the optimal transcoding operation (in both spatial and temporal dimensions) with the highest quality over a diverse range of target bit rates