Optimizing Video Conferencing QoS: A DRL-based Bitrate Allocation Framework

摘要: As the user count for video-related services continues to grow, ensuring high-quality service (QoS) for them will become even more crucial in the future. Many studies have been conducted to enhance the quality of on-demand video streaming using adaptive bitrate (ABR) algorithms and artificial intelligence (AI). This study addresses a more complex challenge than that of on-demand video streaming: enhancing service quality in multi-party, full-duplex communication scenarios, such as video conferences. We propose a deep reinforcement learning (DRL)-based video bitrate allocation framework for a media server in the video conferencing system. Our framework aims to increase overall QoS by applying an appropriate bitrate for each connection in a video conferencing call, considering the network conditions for users. We train the DRL model to maximize the aggregate QoS of users in a meeting by constructing a …