A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks

TL;DR

This paper introduces a new analytical framework for modeling the performance of extended reality applications in edge-assisted wireless networks, addressing complex parameters and heterogeneity, validated with experimental data.

Contribution

It presents a comprehensive performance analysis model for XR in edge networks, considering hardware, software, and network heterogeneity, validated with real testbed data.

Findings

Model accurately predicts XR performance metrics

Addresses heterogeneity in devices and networks

Provides insights into latency and energy trade-offs

Abstract

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models.