Year: 2024
Type: conference-paper
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, research on how these platforms allocate network bandwidth, especially under constrained conditions, and how these resource allocation strategies affect the users' Quality of Experience (QoE) is lacking. This paper addresses this gap by analyzing bandwidth allocation strategies in Zoom, Webex, and Google Meet, with a focus on QoE implications. To assess QoE, we propose a general QoE prediction model based on data collected from a study involving 800 participants. This study is a pioneering effort in evaluating multimedia transmissions across diverse scenarios and network conditions, advancing beyond prior research focused on single media types. The results demonstrate the model's effectiveness and generality in predicting QoE across various VCA scenarios.
Authors: Zejun Zhang, Xiao Zhu, Anlan Zhang, Feng Qian
Institutions: University of Southern California, Google (United States)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: CAREER: Improving Mobile Video Delivery for Emerging Contents and Networks, Collaborative Research: CNS Core: Medium: Foundations and Scalable Algorithms for Personalized and Collaborative Virtual Reality Over Wireless Networks, Collaborative Research: CNS Core: Medium: Innovating Volumetric Video Streaming with Motion Forecasting, Intelligent Upsampling, and QoE Modeling
OpenAlex ID: W4403791491