Energy-Efficient Resource Allocation for Mobile Edge Computing-Based Augmented Reality Applications

TL;DR

This paper proposes a novel resource allocation method for AR applications in mobile edge computing, optimizing communication and computation to significantly reduce mobile energy consumption.

Contribution

It introduces a joint resource allocation approach using Successive Convex Approximation tailored for AR applications in MEC environments, improving energy efficiency.

Findings

Significant reduction in mobile energy consumption

Effective joint optimization of communication and computation resources

Outperforms conventional independent offloading methods

Abstract

Mobile edge computing is a provisioning solution to enable Augmented Reality (AR) applications on mobile devices. AR mobile applications have inherent collaborative properties in terms of data collection in the uplink, computing at the edge, and data delivery in the downlink. In this letter, these features are leveraged to propose a novel resource allocation approach over both communication and computation resources. The approach, implemented via Successive Convex Approximation (SCA), is seen to yield considerable gains in mobile energy consumption as compared to conventional independent offloading across users.