  The increased use of video conferencing applications (VCAs) has made it
critical to understand and support end-user quality of experience (QoE) by all
stakeholders in the VCA ecosystem, especially network operators, who typically
do not have direct access to client software. Existing VCA QoE estimation
methods use passive measurements of application-level Real-time Transport
Protocol (RTP) headers. However, a network operator does not always have access
to RTP headers in all cases, particularly when VCAs use custom RTP protocols
(e.g., Zoom) or due to system constraints (e.g., legacy measurement systems).
Given this challenge, this paper considers the use of more standard features in
the network traffic, namely, IP and UDP headers, to provide per-second
estimates of key VCA QoE metrics such as frames rate and video resolution. We
develop a method that uses machine learning with a combination of flow
statistics (e.g., throughput) and features derived based on the mechanisms used
by the VCAs to fragment video frames into packets. We evaluate our method for
three prevalent VCAs running over WebRTC: Google Meet, Microsoft Teams, and
Cisco Webex. Our evaluation consists of 54,696 seconds of VCA data collected
from both (1), controlled in-lab network conditions, and (2) real-world
networks from 15 households. We show that the ML-based approach yields similar
accuracy compared to the RTP-based methods, despite using only IP/UDP data. For
instance, we can estimate FPS within 2 FPS for up to 83.05% of one-second
intervals in the real-world data, which is only 1.76% lower than using the
application-level RTP headers.
