Symmetrical Multilevel Diversity Coding and Subset Entropy Inequalities
Jinjing Jiang, Neeharika Marukala, and Tie Liu

TL;DR
This paper revisits and extends the optimality proofs of superposition coding in symmetrical multilevel diversity coding (SMDC), introducing new subset entropy inequalities and applying them to more complex scenarios with access or secrecy constraints.
Contribution
It introduces a new sliding-window subset entropy inequality and uses it to strengthen the understanding of superposition coding's optimality in SMDC and related models.
Findings
New sliding-window subset entropy inequality established.
Superposition coding proven optimal for minimum sum rate with weaker requirements.
Extended optimality results to models with additional access or secrecy constraints.
Abstract
Symmetrical multilevel diversity coding (SMDC) is a classical model for coding over distributed storage. In this setting, a simple separate encoding strategy known as superposition coding was shown to be optimal in terms of achieving the minimum sum rate (Roche, Yeung, and Hau, 1997) and the entire admissible rate region (Yeung and Zhang, 1999) of the problem. The proofs utilized carefully constructed induction arguments, for which the classical subset entropy inequality of Han (1978) played a key role. This paper includes two parts. In the first part the existing optimality proofs for classical SMDC are revisited, with a focus on their connections to subset entropy inequalities. First, a new sliding-window subset entropy inequality is introduced and then used to establish the optimality of superposition coding for achieving the minimum sum rate under a weaker source-reconstruction…
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