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

Anik Mallik, Jiang Xie, Zhu Han

TL;DR

The paper tackles the need for a unified end-to-end performance analysis framework for XR in edge-assisted wireless networks, addressing latency, energy, and AoI. It defines an end-to-end latency measure L_tot^q and end-to-end energy E_tot^q across XR pipeline segments, incorporating regression-based resource allocation and CNN complexity considerations, as well as an AoI framework with a RoI metric. The authors contribute explicit, data-driven models for latency, energy, and AoI, including regression forms such as c_{client} and P_{mean}, plus a RoI-based freshness criterion, and validate them on a comprehensive XR testbed with diverse hardware and CNNs. Empirical results show the proposed framework achieves higher accuracy than state-of-the-art models FACT and LEAF+, reducing latency and energy errors by substantial margins, and providing actionable insights for XR system design and deployment in heterogeneous networks.

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.

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

TL;DR

The paper tackles the need for a unified end-to-end performance analysis framework for XR in edge-assisted wireless networks, addressing latency, energy, and AoI. It defines an end-to-end latency measure L_tot^q and end-to-end energy E_tot^q across XR pipeline segments, incorporating regression-based resource allocation and CNN complexity considerations, as well as an AoI framework with a RoI metric. The authors contribute explicit, data-driven models for latency, energy, and AoI, including regression forms such as c_{client} and P_{mean}, plus a RoI-based freshness criterion, and validate them on a comprehensive XR testbed with diverse hardware and CNNs. Empirical results show the proposed framework achieves higher accuracy than state-of-the-art models FACT and LEAF+, reducing latency and energy errors by substantial margins, and providing actionable insights for XR system design and deployment in heterogeneous networks.

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.
Paper Structure (19 sections, 26 equations, 5 figures, 2 tables)

This paper contains 19 sections, 26 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Overview of the Proposed XR Performance Analysis Modeling Framework
  4. The End-to-End Latency Analysis Model
  5. Challenges in Latency Modeling
  6. The Proposed Latency Analysis Model
  7. The Energy Consumption Analysis Model
  8. Challenges in Energy Modeling
  9. The Proposed Energy Consumption Analysis Model
  10. The Age-of-Information (AoI) Analysis Model
  11. Challenges in AoI Modeling
  12. The Proposed AoI Analysis Model
  13. Experimental Setup and Methodology
  14. Performance Evaluation
  15. End-to-End Latency Validation
  16. ...and 4 more sections

Figures (5)

  • Figure 1: XR application pipeline for object detection.
  • Figure 2: External sensor information generation, transmission, and service process for XR.
  • Figure 3: A snippet of the experimental testbed used in this research.
  • Figure 4: Evaluation of the proposed XR performance analysis models: analysis of end-to-end latency for (a) local and (b) remote execution, end-to-end energy consumption for (c) local and (d) remote execution, AoI at different (e) information generation frequency and (f) RoI for info. gen. frequency of $100$ Hz.
  • Figure 5: Comparison of (a) end-to-end latency and (b) end-to-end energy consumption for remote inference obtained from GT and analytical models with FACT liu2018edge and LEAF wang2022leaf+ in terms of normalized accuracy.