Sampling versus random binning for multiple descriptions of a bandlimited source

Random binning is an efficient, yet complex, coding technique for the symmetric L-description source coding problem. We propose an alternative approach, that uses the quantized samples of a bandlimited source as 'descriptions'. By the Nyquist condition, the source can be reconstructed if enough samples are received. We examine a coding scheme that combines sampling and noise-shaped quantization for a scenario in which only K <L descriptions or all L descriptions are received. Some of the received K-sets of descriptions correspond to uniform sampling while others to non-uniform sampling. This scheme achieves the optimum rate-distortion performance for uniform-sampling K-sets, but suffers noise amplification for nonuniform-sampling K-sets. We then show that by increasing the sampling rate and adding a random-binning stage, the optimal operation point is achieved for any K-set.