Sequential random binning for streaming distributed source coding

Abstract

Random binning arguments underlie many results in information theory. In this paper we introduce and analyze a novel type of causal random binning "sequential" binning. This binning is used to get streaming Slepian-Wolf codes with an "anytime" character. At the decoder, the probability of estimation error on any particular symbol goes to zero exponentially fast with delay. In the non-distributed context, we show equivalent results for fixed-rate streaming entropy coding. Because of space constraints, we present full derivations only for the latter, stating the results for the distributed problem. We give bounds on error exponents for both universal and maximum-likelihood decoders