A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks
Anik Mallik, Jiang Xie, Zhu Han

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…
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Taxonomy
TopicsOpportunistic and Delay-Tolerant Networks · Telecommunications and Broadcasting Technologies · Multimedia Communication and Technology
