A polar coding scheme is proposed for the multiple description coding (MDC) problem and is shown to be able to achieve a certain rate pair on the dominant line of the achievable rate region determined by El Gamal and Cover. This scheme is an adaptation of the one developed by ŗaşoğlu et al. for the multiple access channel (MAC) to the MDC setting. The analysis of the proposed scheme contains two new ingredients: 1) a certain MDC-MAC duality and 2) an auxiliary random process that involves both the mutual information and the Bhattacharyya parameter. The decorrelation effect of the polar transform is also investigated.

The problem of sending two correlated vector Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this paper, where each receiver wishes to reconstruct its target source under a covariance distortion constraint. We derive a lower bound on the optimal tradeoff between the transmit power and the achievable reconstruction distortion pair. Our derivation is based on a new bounding technique which involves the introduction of appropriate remote sources. Furthermore, it is shown that this lower bound is achievable by a class of hybrid schemes for the special case, where the weak receiver wishes to reconstruct a scalar source under the mean squared error distortion constraint.

A coding scheme based on polar codes is proposed for the multiple description problem. This scheme is an adaptation of the one developed by ŗaşoğlu et al. for the multiple access channel to the multiple description setting. Specifically, it is shown that the proposed scheme is able to achieve a certain rate pair on the dominant line of the achievable rate region determined by El Gamal and Cover. Due to the dependence between the descriptions, a complication arises in the performance analysis. To resolve this difficulty, a novel technique is introduced which converts dependent descriptions to independent descriptions without affecting the coding rates.

We derive a single-letter lower bound on the minimum sum rate of multiple description coding with symmetric distortion constraints. For the binary uniform source with the erasure distortion measure or Hamming distortion measure, this lower bound can be evaluated with the aid of certain minimax theorems. A similar minimax theorem is established in the quadratic Gaussian setting, which is further leveraged to analyze the special case where the minimum sum rate subject to two levels of distortion constraints (with the second level imposed on the complete set of descriptions) is attained; in particular, we determine the minimum achievable distortions at the intermediate levels.

We derive a single-letter lower bound on the minimum sum rate of multiple description coding with symmetric distortion constraints. For the binary uniform source with the Hamming distortion measure, this lower bound can be evaluated with the aid of a certain minimax theorem. A similar minimax theorem is established in the quadratic Gaussian setting, which is further leveraged to analyze the special case where the minimum sum rate subject to two levels of distortion constraints (with the second level imposed on the complete set of descriptions) is attained; in particular, we determine the minimum achievable distortions at the intermediate levels.

We introduce two new source coding problems: robust sequential coding and robust predictive coding. For the Gauss-Markov source model with the mean squared error distortion measure, we characterize certain supporting hyperplanes of the rate region of these two coding problems. Our investigation also reveals an information-theoretic minimax theorem and the associated extremal inequalities.

The problem of sending a set of correlated Gaussian sources over a bandwidth-matched two-user scalar Gaussian broadcast channel is studied in this work, where the strong receiver wishes to reconstruct several source components (i.e., a vector source) under a distortion covariance matrix constraint and the weak receiver wishes to reconstruct a single source component (i.e., a scalar source) under the mean squared error distortion constraint. We provide a complete characterization of the optimal tradeoff between the transmit power and the achievable reconstruction distortion pair for this problem. The converse part is based on a new bounding technique which involves the introduction of an appropriate remote source. The forward part is based on a hybrid scheme where the digital portion uses dirty paper channel code and Wyner-Ziv source code. This scheme is different from the optimal scheme proposed by Tian et al. in a recent work for the scalar-scalar case, which implies that the optimal scheme for the scalar-scalar case is in fact not unique.

We introduce two new source coding problems: robust sequential coding and robust predictive coding. For the Gauss-Markov source model, we characterize certain supporting hyperplanes of the rate region of these two coding problems. Our investigation also reveals a class of extremal inequalities and minimax theorems.