Full Characterization of Optimal Uncoded Placement for the Structured Clique Cover Delivery of Nonuniform Demands

TL;DR

This paper analytically characterizes the optimal uncoded placement strategy for nonuniform demands in coded caching, revealing that a two or three group approach minimizes expected delivery rate under storage constraints.

Contribution

It introduces a precise optimal placement strategy for nonuniform demands, including a polynomial time algorithm to determine base-cases and analytical derivation of optimal memory allocation.

Findings

Optimal placement follows two or three group strategies.

Two group strategy is always optimal for base-cases.

Memory sharing achieves optimal placement for other storage capacities.

Abstract

We investigate the problem of coded caching for nonuniform demands when the structured clique cover algorithm proposed by Maddah-Ali and Niesen for decentralized caching is used for delivery. We apply this algorithm to all user demands regardless of their request probabilities. This allows for coding among the files that have different request probabilities but makes the allocation of memory to different files challenging during the content placement phase. As our main contribution, we analytically characterize the optimal placement strategy that minimizes the expected delivery rate under a storage capacity constraint. It is shown that the optimal placement follows either a two or a three group strategy, where a set of less popular files are not cached at all and the files within each of the other sets are allocated identical amounts of storage as if they had the same request probabilities. We show that for a finite set of storage capacities, that we call the base-cases of the problem, the two group strategy is always optimal. For other storage capacities, optimal placement is achieved by memory sharing between certain base-cases and the resulting placement either follows a two or a three group strategy depending on the corresponding base-cases used. We derive a polynomial time algorithm that determines the base-cases of the problem given the number of caches and popularity distribution of files. Given the base-cases of the problem, the optimal memory allocation parameters for any storage capacity are derived analytically.