# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
###### Abstract
used the signal-to-noise ratio (SNR) as the QoS metric and peak signal to noise ratio (PSNR) as the QoE metric to calculate experiential capacity regions and improve the QoE of Skype video conferencing on Wi-Fi networks by limiting the number of clients serviced [4]. On the other hand, [PERSON] presented a conceptual model that used the term PQoS to monitor the level of service acceptability and the correlation between user-perceived quality and delivered quality [5]. Thus, it is desirable to distinguish between methods using only performance parameters and those leveraging user surveys by using the terms PQoS and QoE. To our knowledge, related works have used external measurements to collect network performance parameters or legacy 802.11a/b/g/n standards to produce datasets for training machine learning (ML) algorithms. Others have performed end-to-end measurements of generic network performance parameters, such as delay, jitter, and bandwidth, which cannot distinguish quality degradation between wired and wireless networks. Furthermore, related works have used cloud computing methodology to collect and analyze raw data on ISPs' infrastructure, raising privacy concerns regarding data storage, sharing, and analytics purposes. This paper presents methods to estimate the PQoS of video conferencing in WebRTC communication over Wi-Fi networks using ML. The ML algorithms were trained with four datasets to achieve reliable results. WebRTC application parameters, such as delay and packet loss, were collected from each client during video conferencing to calculate the level of PQoS in the form of MOS values in the range of 1-4 (1 = bad, 2 = poor, 3 = fair, 4 = good). A series of experiments was performed to produce datasets comprising 802.11g/n/ac/ax-specific network performance parameters labeled with MOS values. Finally, supervised learning algorithms were trained to select the best ML algorithm for estimating the PQoS of video conferencing on Wi-Fi networks. Utilizing these methods, the proposed approach classified the PQoS of video conferencing with at least 92% accuracy. Indeed, this study demonstrated that 802.11-specific network performance parameters can reflect user-perceived quality of video conferencing on Wi-Fi networks. Since datasets initially comprised more than 240 features, which could increase the complexity of ML training, feature engineering techniques were used to reduce the dimensions of the datasets. Seven ML algorithms were then chosen to be trained with the reduced datasets. Of these, the REPTree algorithm performed best regarding accuracy, interpretability, and computational cost criteria. Finally, changes that occurredin features of the produced model were investigated to identify the cause of poor quality on 802.11 networks. This ML model can be executed as a script on Wi-Fi access points (APs) to estimate the PQoS of video conferencing services on the network edge and to send the MOS value and the related parameters during quality degradation to the ISPs' cloud infrastructure for further analysis. ISPs can thus utilize the results of this study to continuously monitor PQoS of video conferencing on customer premises and even identify anomalies or any quality degradation on wireless networks. In doing so, they will be better equipped to deliver predictable, measurable wireless Internet quality for home users while preserving customers' privacy and reducing the operational costs of analytics. Ultimately, this study could be extended to consider a combination of different services in homes and buildings and to provide an extensive quality assessment approach. The rest of this paper is organized as follows: tools and methodology are described in Section 2. The results of data collection and training ML algorithms are presented in Section 3. Section 4 discusses the results and implications of the ML model, and Section 5 presents related work for estimating QoE or PQoS using ML. Finally, Section 6 concludes the study and presents a roadmap for future research.