On the accuracy and complexity of rate-distortion models for fine-grained scalable video sequences
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Distributed media rate allocation in multipath networks
Image Communication
Quality modeling for the medium grain scalability option of H.264/SVC
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
A frame bit allocation algorithm in mobile P2P overlay network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Journal of Visual Communication and Image Representation
Journal of Systems and Software
INFOCOM'10 Proceedings of the 29th conference on Information communications
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Rate-distortion (R-D) modeling of video coders has always been an important issue in video streaming; however, few of the traditional R-D models and their performance have been closely examined in the context of scalable (FGS-like) video. To overcome this shortcoming, the first half of the paper models rate-distortion of DCT-based fine-granular scalable coders and derives a simple operational R-D model for Internet streaming applications. Experimental results demonstrate that this R-D result, an extension of the classical R-D formula, is very accurate within the domain of scalable coding methods exemplified by MPEG-4 FGS and H.264 progressive FGS. In the second half of the paper, we examine congestion control and dynamic rate-scaling algorithms that achieve smooth visual quality during streaming using the proposed R-D model. In constant bitrate (CBR) channels, our R-D based quality-control algorithm dramatically reduces PSNR variation between adjacent frames (to less than 0.1 dB in sample sequences). Since the Internet is a changing environment shared by many sources, even R-D based quality control often cannot guarantee nonfluctuating PSNR in variable-bitrate (VBR) channels without the help from an appropriate congestion controller. Thus, we apply recent utility-based congestion control methods to our problem and show how a combination of this approach and our R-D model can benefit future streaming applications