Adaptive robust video broadcast via satellite

Abstract

With increasing demand for multimedia content over channels with limited bandwidth and heavy packet losses, higher coding efficiency and stronger error resiliency is required more than ever before. Both the coding efficiency and error resiliency are two opposing processes that require appropriate balancing. On the source encoding side the video encoder H.264/AVC can provide higher compression with strong error resiliency, while on the channel error correction coding side the raptor code has proven its effectiveness, with only modest overhead required for the recovery of lost data. This paper compares the efficiency and overhead of both the raptor codes and the error resiliency techniques of video standards so that both can be balanced for better compression and quality. The result is also improved by confining the robust stream to the period of poor channel conditions by adaptively switching between the video streams using switching frames introduced in H.264/AVC. In this case the video stream is initially transmitted without error resiliency assuming the channel to be completely error free, and then the robustness is increased based on the channel conditions and/or user demand. The results showed that although switching can increase the peak signal to noise ratio in the presence of losses but at the same time its excessive repetition can be irritating to the viewers. Therefore to evaluate the perceptual quality of the video streams and to find the optimum number of switching during a session, these streams were scored by different viewers for quality of enhancement. The results of the proposed scheme show an increase of 3 to 4 dB in peak signal to noise ratio with acceptable quality of enhancement.

DOI: 10.1007/s11042-016-3426-y

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Cite this paper

@article{Altaf2016AdaptiveRV, title={Adaptive robust video broadcast via satellite}, author={Muhammad Altaf and Farman Ali Khan and Nadia N. Qadri and Mohammed Ghanbari and Sandra E. M. Dudley}, journal={Multimedia Tools and Applications}, year={2016}, volume={76}, pages={7785-7801} }