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

arXiv: https://arxiv.org/abs/2405.07033 · HTML: https://mlsanity.com/paper/2405.07033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 9 of 9)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices](https://mlsanity.com/paper/2310.18329/) (2023)
- [LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality](https://mlsanity.com/paper/2205.13770/) (2022)
- [Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications](https://mlsanity.com/paper/2011.13075/) (2020)
- [Wireless Edge Computing with Latency and Reliability Guarantees](https://mlsanity.com/paper/1905.05316/) (2019)
- [EPAM: A Predictive Energy Model for Mobile AI](https://mlsanity.com/paper/2303.01509/) (2023)
- [Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning](https://mlsanity.com/paper/2004.04640/) (2020)
- [Age-of-Information for Computation-Intensive Messages in Mobile Edge Computing](https://mlsanity.com/paper/1901.01854/) (2019)
- [Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles](https://mlsanity.com/paper/2403.08931/) (2024)

## Abstract (arXiv)

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.