# Joint Pushing and Caching for Bandwidth Utilization Maximization in Wireless Networks

## TL;DR

This paper develops an optimal joint pushing and caching strategy for wireless networks to maximize bandwidth utilization by leveraging user demand predictability and cache resources, providing both theoretical insights and practical algorithms.

## Contribution

It formulates a stochastic optimization problem, analyzes the structure of the optimal policy, and proposes a low-complexity decentralized solution with an online learning algorithm.

## Key findings

- Optimal policy balances current and future transmission costs.
- Increasing cache size reduces average transmission cost.
- Proposed decentralized and online algorithms outperform existing methods.

## Abstract

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, by exploiting storage resources at end users and predictability of user demand processes, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers. In particular, we formulate the stochastic optimization problem as an infinite horizon average cost Markov Decision Process (MDP), for which there generally exist only numerical solutions without many insights. By structural analysis, we show how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. In addition, we show that the optimal average transmission cost decreases with the cache size, revealing a tradeoff between the cache size and the bandwidth utilization. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a decentralized policy with polynomial complexity w.r.t. the numbers of users and files as well as cache size, by a linear approximation of the value function and optimization relaxation techniques. Next, we propose an online decentralized algorithm to implement the proposed low-complexity decentralized policy using the technique of Q-learning, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled content-centric wireless networks.