Scalable Ground-Truth Annotation for Video QoE Modeling in Enterprise WiFi

Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of myriads of applications from video telephony and streaming to virtual reality, network administrators face the challenge to provide high quality of experience (QoE) in the face of diverse wireless conditions and application contents. Yet, state-of-the-art networks lack analytics for QoE, as this requires support from the application or user feedback. While there are existing techniques to map quality of service (QoS) to QoE by training machine learning (ML) models without requiring user feedback, these techniques are limited to only few applications (e.g., Skype), due to insufficient QoE ground-truth annotation for ML. To address these limitations, we focus on video telephony applications and model key artefacts of spatial and temporal video QoE. Our key contribution is designing content- and device-independent metrics and training across diverse WiFi conditions. We show that our metrics achieve a median 90% accuracy by comparing with mean-opinion-score (MOS) from more than 200 users and 800 video samples. Our content-independent metrics significantly reduce the MOS prediction error of previous works and are validated over three popular video telephony applications - Skype, FaceTime and Google Hangouts.