Exploring diverse measures for evaluating QoE in the context of WebRTC

Enabling high Quality of Experience (QoE) with video-conferencing solutions based on Web Real-Time Communication (WebRTC) protocols anywhere, anytime, is challenging and triggers the exploration of new ways to gain QoE insights. In this paper, we share initial observations from a within-subjects experiment (N = 22) in which 2-party WebRTC-based audiovisual conversations took place under varying technical conditions. We collected self-report data, peripheral physiological data and application-level performance statistics with the overall aim of exploring their usefulness and compatibility for assessing QoE in the context of WebRTC. Our preliminary findings indicate that the varying quality is well-reflected in the self-reported overall quality and annoyance. However, this might not apply when considering the emotional valence ratings and ECG data, implying that other factors may play a role here.