Joint source-channel decoding of convolutionally encoded multiple-descriptions

The scenario considered in this paper is the transmission of a continuous information source over a set of erasure channels, by using a multiple description quantizer to deal with channel erasures and a convolutional channel code on each channel to deal with random bit errors. The diversity available in multiple descriptions is subsequently exploited in Viterbi sequence detectors to jointly decode the convolutional codes. Two approaches to joint decoding are presented and investigated. Simulation results are presented for two-channel multiple description quantization of Gaussian sources which demonstrate the potential improvements in end-to-end source distortion achievable with joint decoding of channel codes in a multiple description system. We also compare the performance of joint Viterbi detectors with that of turbo-style iterative decoding of multiple-description codes proposed earlier.