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This paper is about the design and analysis of an index-assignment (IA) basedmultiple-description coding scheme for the n-channel asymmetric case. We useentropy constrained lattice vector quantization and restrict attention tosimple reconstruction functions, which are given by the inverse IA functionwhen all descriptions are received or otherwise by a weighted average of thereceived descriptions. We consider smooth sources with finite differentialentropy rate and MSE fidelity criterion. As in previous designs, ourconstruction is based on nested lattices which are combined through a single IAfunction. The results are exact under high-resolution conditions andasymptotically as the nesting ratios of the lattices approach infinity. For anyn, the design is asymptotically optimal within the class of IA-based schemes.Moreover, in the case of two descriptions and finite lattice vector dimensionsgreater than one, the performance is strictly better than that of existingdesigns. In the case of three descriptions, we show that in the limit of largelattice vector dimensions, points on the inner bound of Pradhan et al. can beachieved. Furthermore, for three descriptions and finite lattice vectordimensions, we show that the IA-based approach yields, in the symmetric case, asmaller rate loss than the recently proposed source-splitting approach.

n-Channel Asymmetric Entropy-Constrained Multiple-Description Lattice Vector Quantization