# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 5 Related Work  
As video conferencing applications have continued to grow in popularity, particularly during the pandemic, researchers have investigated the perceived quality of video conferencing over wireless networks to improve quality of experience (QoE) using ML. A few of these papers have investigated perceived quality, specifically for Wi-Fi networks in their experimental setups, whereas others have considered wired networks. In their survey of the literature predicting the PQoS using ML on Wi-Fi networks, [PERSON] and [PERSON] noted that the majority of works had used external measurements, such as packet captures and ping tests, to collect generic network performance parameters [22]. Moreover, research efforts have leveraged different sets of parameters to estimate the perceived quality of video conferencing over wireless networks, which, in turn, has led them to select different quality assessment methods. [PERSON] et al. investigated the prediction of QoE of WebRTC over Wi-Fi networks. They devised an experiment involving a commodity AP and two clients, in which one connected wirelessly and the other connected through a wired setup to a joint WebRTC session. They performed 620 experiments by moving the wireless client between rooms on a building floor. Meanwhile, they collected wireless performance parameters, such as RSSI, link quality, signal quality, packet loss ratio, and RTT, for the Windows client. Next, they used the time interval between two consecutively played video frames to identify video freezing, and categorized frames with a time interval between two frames greater than one second as bad QoE and less than one second as good. They further categorized experiments as unacceptable when a session experienced bad QoE for more than 30% of the session. Using the decision tree, random forest, SVM, and extra tree classifiers with an 80-20% training-testing split, they achieved the highest F1 score, 0.74, with the decision tree [17; 18]. [PERSON] et al. investigated the QoE of WebRTC in mobile broadband networks using the MONROE platform. They implemented a media server using Kurento in such a way that PC clients could connect to the media server for establishing WebRTC sessions. These researchers collected QoS parameters, including jitter, packet loss, bytes received, and RTT, which produced a dataset with 20,000 instances. After inviting 28 participants to assess the quality of 15% of the dataset in the form of MOS scores, they leveraged various ML regression algorithms, such as random forest, SVR, regression tree, and AdaBoost, to estimate the MOS value of 85% of the dataset. The results indicated that a daily average of QoE was between 4 and 4.5, suggesting that quality was good or excellent most of the time [19]. In another study, [PERSON] et al. investigated an application of ML for estimating the performance of WebRTC and identifying the root cause of quality degradation. After designing lab experiments with three wired clients and a network emulator to generate network impairments, they invited 12 participants to rate the WebRTC quality. They collected WebRTC-internal statistics from web browsers and attempted to categorize video blockiness and audio distortion into acceptable and unacceptable classes. They leveraged C4.5, random forest, naive Bayes, SMO, IBK, and bagging in WEKA and achieved 92% and 82% accuracy in categorizing video blockiness and audio distortion into binary classes, respectively [20]. On the other hand, [PERSON] et al. investigated the perceived quality of RTC video over Wi-Fi networks by measuring the SSIM of video and the PESQ of audio communications. They collected QoS parameters, such as medium busy time, physical rate of the last frame, number of transmitted and retransmitted frames, and RSSI, and achieved an RSME of 0.6-0.79 by running a support vector regression (SVR) [3]. Since Skype is one of the most popular video conferencing services, [PERSON] et al. investigated the impact of various network conditions on Skype video calls.