Discerning MOS of Video Conferencing via Deep Packet Inspection and Video Context Clues

Monitoring the Mean Opinion Score (MOS) of video conferencing is critical for Internet Service Providers (ISPs) to ensure user satisfaction. However, a significant technical challenge arises: MOS is a subjective measure, while ISPs primarily rely on deep packet inspection (DPI) data for performance monitoring, making direct mapping between MOS and DPI data nearly impossible. To address this gap, we develop DePI-MOSE, a novel solution that leverages sub-application-level video context clues to build machine-learning models. By inferring the type of end devices and identifying the motion level within video content during a conference session, DePI-MOSE can estimate MOS values from DPI data accurately. We implemented and tested DePI-MOSE in a real-world ISP network, and experimental results show that DePI-MOSE is more accurate than state-of-the-art methods. We also built a network resource management platform for ISPs to dynamically adjust users' network resources by precisely monitoring users' video conferencing QoE.