Hit Ratio Driven Mobile Edge Caching Scheme for Video on Demand Services

TL;DR

This paper proposes an optimal cache placement strategy for mobile edge computing in video on demand services, aiming to maximize cache hit ratio by considering video popularity, user preferences, and video characteristics.

Contribution

It introduces a novel cache placement model that accounts for multiple factors influencing video request probabilities and provides an optimal solution via dynamic programming.

Findings

Significant improvement in cache hit ratio with the proposed algorithm

Effective modeling of video request probability considering multiple factors

Optimal caching strategy derived from a grouping knapsack problem

Abstract

More and more scholars focus on mobile edge computing (MEC) technology, because the strong storage and computing capabilities of MEC servers can reduce the long transmission delay, bandwidth waste, energy consumption, and privacy leaks in the data transmission process. In this paper, we study the cache placement problem to determine how to cache videos and which videos to be cached in a mobile edge computing system. First, we derive the video request probability by taking into account video popularity, user preference and the characteristic of video representations. Second, based on the acquired request probability, we formulate a cache placement problem with the objective to maximize the cache hit ratio subject to the storage capacity constraints. Finally, in order to solve the formulated problem, we transform it into a grouping knapsack problem and develop a dynamic programming algorithm to obtain the optimal caching strategy. Simulation results show that the proposed algorithm can greatly improve the cache hit ratio.