Spatio-Temporal Iterative Source–Channel Decoding Aided Video Transmission

Abstract

Low-complexity uncompressed video transmission meets the requirements of home networking and quality/delay-sensitive medical applications. Hence, it has attracted research attention in recent years. The redundancy inherent in the uncompressed video signals may be exploited by joint source–channel decoding to improve the attainable error resilience. Hence, in this treatise, we study the application of iterative joint source–channel decoding aided uncompressed video transmission, where correlation inherent in the video signals is modeled by a first-order Markov process. First, we propose a spatiotemporal joint source–channel decoding system using a recursive systematic convolutional codec, where both the horizontal and vertical intraframe correlations, as well as the interframe correlations, are exploited by the receiver, hence relying on 3-D information exchange. This scheme may be combined with arbitrary channel codecs. Then, we analyze the three-stage decoder's convergence behavior using 3-D extrinsic information transfer (EXIT) charts. Finally, we benchmark the attainable system performance against a couple of video communication systems, including our previously proposed 2-D scheme, where only intraframe correlations were exploited without invoking a channel codec. Our simulation results show that substantial $E_{b}/N_{0}$ improvements are attainable by the proposed technique.