Iterative Decoding of Convolutionally Encoded Multiple Descriptions

Abstract

Transmission of convolutionally encoded multiple descriptions over noisy channels can bene¿t from the use of iterative source-channel decoding methods. This paper investigates the combined use of time-dependencies and inter-description correlation incurred by the multiple description scalar quantizer. We ¿rst modi¿ed the BCJR algorithm in a way that symbol a posteriori probabilities can be derived and used as extrinsic information to help iterative decoding between channel and source decoders. Also proposed is a recursive implementation for the source decoder that exploits the inter-description correlation to jointly decode multiple descriptions. Simulation results indicate that our proposed scheme can achieve signi¿cant improvement over the bit-level iterative decoding schemes.