Virtual Reality Traffic Prioritization for Wi-Fi Quality of Service Improvement using Machine Learning Classification Techniques

TL;DR

The paper tackles the challenge of delivering low‑latency XR/VR over Wi‑Fi by proposing a supervised, ML‑based VR traffic classifier that differentiates interactive VR traffic from Non‑VR traffic using features that capture downlink/uplink relationships. It evaluates six classifiers with permutation‑based feature selection and grid‑search hyperparameter tuning on VR and Non‑VR traces collected in a Cloud‑Edge VR edge streaming setup. The best configuration, $\omega=\omega$ and $N=$, achieves high validation accuracy ($>0.98$) with per‑sample processing under 1 s, and generalizes to multi‑user ALVR and Steam Link scenarios. A Wi‑Fi QoS simulation demonstrates that identifying VR traffic enables prioritization that reduces VR delays by about 4.2×, while BG traffic experiences increased delays, highlighting the trade‑offs of traffic prioritization. The work suggests practical viability for deploying VR‑aware QoS in APs and outlines directions for broader VR trace coverage and consideration of real‑world network effects.

Abstract

The increase in the demand for eXtended Reality (XR)/Virtual Reality (VR) services in the recent years, poses a great challenge for Wi-Fi networks to maintain the strict latency requirements. In VR over Wi-Fi, latency is a significant issue. In fact, VR users expect instantaneous responses to their interactions, and any noticeable delay can disrupt user experience. Such disruptions can cause motion sickness, and users might end up quitting the service. Differentiating interactive VR traffic from Non-VR traffic within a Wi-Fi network can aim to decrease latency for VR users and improve Wi-Fi Quality of Service (QoS) with giving priority to VR users in the access point (AP) and efficiently handle VR traffic. In this paper, we propose a machine learning-based approach for identifying interactive VR traffic in a Cloud-Edge VR scenario. The correlation between downlink and uplink is crucial in our study. First, we extract features from single-user traffic characteristics and then, we compare six common classification techniques (i.e., Logistic Regression, Support Vector Machines, k-Nearest Neighbors, Decision Trees, Random Forest, and Naive Bayes). For each classifier, a process of hyperparameter tuning and feature selection, namely permutation importance is applied. The model created is evaluated using datasets generated by different VR applications, including both single and multi-user cases. Then, a Wi-Fi network simulator is used to analyze the VR traffic identification and prioritization QoS improvements. Our simulation results show that we successfully reduce VR traffic delays by a factor of 4.2x compared to scenarios without prioritization, while incurring only a 2.3x increase in delay for background (BG) traffic related to Non-VR services.

Figures (3)

• Figure 4: Testing the Interactive VR Traffic Identification Model in a Multi-user Experimental Setup.
• Figure 5: System operation example: VR traffic classification and prioritization.
• Figure 6: A comparison of traffic packet delay for VR and BG traffic (DL) in both medium and worse delay scenarios, with and without VR prioritization.