Optimal Dynamic Multicast Scheduling for Cache-Enabled Content-Centric Wireless Networks

TL;DR

This paper develops an optimal dynamic multicast scheduling framework for cache-enabled content-centric wireless networks, minimizing delay, power, and fetching costs by analyzing the structure of the optimal policy and proposing low-complexity algorithms.

Contribution

It introduces a novel joint scheduling approach using MDP analysis, revealing structural properties of the optimal policy and proposing efficient algorithms for implementation.

Findings

Optimal policies have switch structures in uniform and nonuniform cases.

Monotonically non-decreasing switch curves for two-content uniform case.

Low-complexity algorithms achieve near-optimal performance.

Abstract

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not make full use of the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). It is well-known to be a difficult problem due to the curse of dimensionality, and there generally only exist numerical solutions. By using relative value iteration algorithm and the special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Then, by exploiting these structural properties of the optimal policy, we propose two low-complexity optimal algorithms. Motivated by the switch structures of the optimal policy, to further reduce the complexity, we also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to compute this policy.