Computer Science > Networking and Internet Architecture
arXiv:2408.08617 (cs)
[Submitted on 16 Aug 2024]
Title:Virtual Reality Traffic Prioritization for Wi-Fi Quality of Service Improvement using Machine Learning Classification Techniques
Authors:Seyedeh Soheila Shaabanzadeh (1),Marc Carrascosa-Zamacois (2),Juan Sánchez-González (1),Costas Michaelides (2),Boris Bellalta (2) ((1) Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, (2) Universitat Pompeu Fabra (UPF), Barcelona, Spain)
View a PDF of the paper titled Virtual Reality Traffic Prioritization for Wi-Fi Quality of Service Improvement using Machine Learning Classification Techniques, by Seyedeh Soheila Shaabanzadeh (1) and 8 other authors
View PDFHTML (experimental)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.
| Comments: | 19 pages, 6 figures |
| Subjects: | Networking and Internet Architecture (cs.NI) |
| Cite as: | arXiv:2408.08617 [cs.NI] |
| (orarXiv:2408.08617v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2408.08617 arXiv-issued DOI via DataCite | |
| Journal reference: | Journal of Network and Computer Applications, Volume 230, October 2024, 103939 |
| Related DOI: | https://doi.org/10.1016/j.jnca.2024.103939 DOI(s) linking to related resources |
Submission history
From: Seyedeh Soheila Shaabanzadeh [view email][v1] Fri, 16 Aug 2024 09:17:32 UTC (2,367 KB)
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View a PDF of the paper titled Virtual Reality Traffic Prioritization for Wi-Fi Quality of Service Improvement using Machine Learning Classification Techniques, by Seyedeh Soheila Shaabanzadeh (1) and 8 other authors
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