  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.
