The main objective of this thesis is to investigate the problem of joint source-channel decoding in a system based on multiple description quantization and to develop efficient decoding schemes. A review of the previously proposed joint decoding schemes is given with an experimental evaluation of their performance. Subsequently, a system with two channel multiple description quantization is considered in which the output of a quantizer is convolutional encoded prior to transmission. Based on the idea of concatenated coding, a joint decoding scheme that uses list Viterbi algorithm is presented. Experimental results show that this scheme is capable of achieving a significant improvement in the error correcting capability of the convolutional code at moderate complexities. A previously proposed optimum trellis decoding scheme which performs decoding at symbol level is extended to a bit level decoding scheme. It is shown that bit level decoding in this manner considerably reduces the computational complexity. (Abstract shortened by UMI.)
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