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.

中文翻译：

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使用机器学习分类技术提高 Wi-Fi 服务质量的虚拟现实流量优先级


近年来，扩展现实（XR）/虚拟现实（VR）服务需求的增加，对Wi-Fi网络维持严格的延迟要求提出了巨大的挑战。在 Wi-Fi 虚拟现实中，延迟是一个重要问题。事实上，VR 用户期望他们的交互能够得到即时响应，任何明显的延迟都会扰乱用户体验。此类中断可能会导致晕动病，用户最终可能会退出该服务。将 Wi-Fi 网络内的交互式 VR 流量与非 VR 流量区分开来，旨在减少 VR 用户的延迟并提高 Wi-Fi 服务质量 (QoS)，同时优先考虑接入点 (AP) 中的 VR 用户并有效处理VR交通。在本文中，我们提出了一种基于机器学习的方法，用于识别云边缘 VR 场景中的交互式 VR 流量。下行链路和上行链路之间的相关性在我们的研究中至关重要。首先，我们从单用户流量特征中提取特征，然后比较六种常见的分类技术（即逻辑回归、支持向量机、k 最近邻、决策树、随机森林和朴素贝叶斯）。对于每个分类器，应用超参数调整和特征选择的过程，即排列重要性。使用不同 VR 应用程序（包括单用户和多用户案例）生成的数据集来评估创建的模型。然后，使用 Wi-Fi 网络模拟器来分析 VR 流量识别和优先级 QoS 改进。我们的模拟结果表明，与没有优先级的场景相比，我们成功地将 VR 流量延迟减少了 4.2 倍，而与非 VR 服务相关的背景 (BG) 流量的延迟仅增加了 2.3 倍。