# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 6 Conclusions  
Although ISFs deploy fibers to deliver a higher quality of service, the best-effort nature of Wi-Fi networks as the last hop to the end user may prevent clients from experiencing maximum video conferencing quality. Existing approaches for estimating the perceived quality of video conferencing often leverage external measurements to collect generic network performance parameters, such as delay, jitter, and packet loss, for training ML algorithms on the cloud infrastructure of service providers. However, external measurements may not be convenient for users, and cloud analytics can impose security concerns and excessive costs for ISFs. Therefore, this paper presented a novel approach for estimating the PQoS of video conferencing services over Wi-Fi using 802.11-specific network performance parameters on customer premises. More than 16 video conferencing experiments were performed on Wi-Fi networks operating at 802.11g/n/ac/ax standards over 2.4 GHz and 5 GHz frequency bands to produce datasets containing more than 40,000 instances in total. After applying feature engineering techniques to reduce the number of features, ML algorithms classified the level of PQoS with 92-98% accuracy. In terms of accuracy, interpretability, and computation criteria, the REPTree was the best algorithm for estimating the PQoS of video conferencing. The produced ML model can be executed on the Wi-Fi APs as a lightweight script to continuously classify the level of PQoS on customer premises in the form of edge computing. The script calculates the PQoS in the form of MOS values and reports it to the customer support dashboard. When wireless quality degrades, the script can send the 802.11-specific network performance parameters, which correlate with quality degradation, to the cloud infrastructure of ISFs for further analysis by support and operational teams. Thus, ISFs can utilize this paper's approach to deliver predictable, measurable wireless quality in homes and buildings prepared for carrier-grade Internet connectivity and upcoming novel services. With our unique approach, ISFs can also address clients' privacy concerns regarding data analytics while reducing the operational costs of data analytics for themselves.  
Although this study performed real-life experiments of video conferencing services in homes and buildings, it could not capture all wireless conditions or incorporate them into the training datasets. Therefore, future studies should collect data from various locations to improve the precision of the ML model. Furthermore, this study focused only on video conferencing services, whereas residential clients use a combination of services, such as video streaming, video conferencing, and web browsing. Therefore, the authors aim to extend this study to estimate the PQoS of wireless connectivity for various services in homes and buildings.  
**Author Contributions:** Conceptualization, M.M. and J.N.; methodology, M.M.; software, M.M.; validation, M.M. and J.N.; formal analysis, M.M.; investigation, M.M.; resources, M.M.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, J.N.; visualization, M.M.; supervision, J.N.; project administration, J.N.; funding acquisition, J.N. Both authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was supported by the Research Council of Norway, grant number [272304]. The project was co-financed by the Research Council of Norway and EyeNetworks AS.  
**Data Availability Statement:** The datasets used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.