Human-Centric Resource Allocation in the Metaverse over Wireless Communications

TL;DR

This paper develops a novel optimization framework for resource allocation in the human-centric Metaverse over wireless networks, balancing user experience and system costs, with a new fractional programming method.

Contribution

It introduces a new utility measure based on perceptual VR quality, formulates a non-convex optimization problem, and proposes a fractional programming algorithm with broad applicability.

Findings

The proposed algorithm outperforms existing approaches in simulations.

The utility measure accurately reflects user perception of VR quality.

The fractional programming technique advances optimization methods for complex problems.

Abstract

The Metaverse will provide numerous immersive applications for human users, by consolidating technologies like extended reality (XR), video streaming, and cellular networks. Optimizing wireless communications to enable the human-centric Metaverse is important to satisfy the demands of mobile users. In this paper, we formulate the optimization of the system utility-cost ratio (UCR) for the Metaverse over wireless networks. Our human-centric utility measure for virtual reality (VR) applications of the Metaverse represents users' perceptual assessment of the VR video quality as a function of the data rate and the video resolution, and is learnt from real datasets. The variables jointly optimized in our problem include the allocation of both communication and computation resources as well as VR video resolutions. The system cost in our problem comprises the energy consumption and delay, and is non-convex with respect to the optimization variables due to fractions in the mathematical expressions. To solve the non-convex optimization, we develop a novel fractional programming technique, which contributes to optimization theory and has broad applicability beyond our paper. Our proposed algorithm for the system UCR optimization is computationally efficient and finds a stationary point to the constrained optimization. Through extensive simulations, our algorithm is demonstrated to outperform other approaches.