Towards Individual QoE for Multiparty Videoconferencing

Videoconferencing is becoming an essential part in everyday life. The visual channel allows us for interactions that were not possible over audio-only communication systems, such as the telephone. However, being a de-facto over-the-top service, the quality of the delivered videoconferencing experience is subject to variations, depending on network conditions. Videoconferencing systems adapt to network conditions by changing, for example, encoding bit rate of the video. For this adaptation not to hamper the benefits related to the presence of a video channel in the communication, it needs to be optimized according to a measure of the quality of experience (QoE) as perceived by the user. The latter is highly dependent on the ongoing interaction and individual preferences, which have hardly been investigated so far. In this paper, we focus on the impact that video quality has on conversations that revolve around objects that are presented over the video channel. To this end, we conducted an empirical study where groups of four people collaboratively build a Lego model over a videoconferencing system. We examine the requirements for such a task by showing when the interaction, measured by visual and auditory cues, changes depending on the encoding bit rate and loss. We then explore the impact that prior experience with the technology and affective state have on QoE of participants. We use these factors to construct predictive models that double the accuracy compared to a model based on the system factors alone. We conclude with a discussion of how these factors could be applied in real-world scenarios.