An In-depth Study of Bandwidth Allocation across Media Sources in Video Conferencing

Zejun Zhang, Xiao Zhu, Anlan Zhang, Feng Qian

Abstract: Video Conferencing Applications (VCAs) are indispensable for real-time communication in remote work and education by enabling simultaneous transmission of audio, video, and screen-sharing content. Despite their ubiquity, there is a noticeable lack of research on how these platforms allocate resources, especially under limited bandwidth constraints, and how these resource allocation strategies affect the Quality of Experience (QoE). This paper addresses this research gap by conducting an in-depth analysis of bandwidth allocation strategies among prominent VCAs, including Zoom, Webex, and Google Meet, with an emphasis on their implications for QoE. To assess QoE effectively, we propose a general QoE model based on data collected from a user study involving over 800 participants. This study marks a pioneering effort in the extensive evaluation of multimedia transmissions across diverse scenarios for VCAs, representing a significant advancement over prior research that predominantly concentrated on the quality assessment of singular media types. The promising outcomes highlight the model's effectiveness and generalization in accurately predicting Quality of Experience (QoE) across various scenarios among VCAs.

Primary Subject Area: [Experience] Interactions and Quality of Experience

Secondary Subject Area: [Experience] Multimedia Applications

Relevance To Conference: Our paper focuses on three media sources: audio, video, and screen-sharing. We aim to analyze their concurrent transmission and propose a QoE prediction model for multimedia transmission. This model is generalizable and can be applied to evaluate and compare QoE across different VCAs and in a range of scenarios. It delivers critical insights and recommendations, empowering VCAs to enhance user experience by aligning their services more closely with the optimal user preferences, especially in environments with limited network capacity.

Submission Number: 2167