Handcrafted vs Deep Learning Classification for Scalable Video QoE Modeling
This addresses the challenge for network administrators to provide high QoE in wireless networks by enabling scalable analytics without relying on application support or user feedback, though it is incremental as it builds on existing machine learning techniques.
The paper tackles the problem of modeling video Quality of Experience (QoE) for telephony applications without user feedback by designing content- and device-independent metrics, achieving 90% median accuracy compared to user scores, and improving to 95% accuracy with a deep learning model, a 38% gain over state-of-the-art methods.
Mobile video traffic is dominant in cellular and enterprise wireless networks. With the advent of diverse applications, network administrators face the challenge to provide high 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 QoS to QoE by training machine learning models without requiring user feedback, these techniques are limited to only few applications, 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 from more than 200 users and 800 video samples over three popular video telephony applications -- Skype, FaceTime and Google Hangouts. We further extend our metrics by using deep neural networks, more specifically we use a combined CNN and LSTM model. We achieve a median accuracy of 95% by combining our QoE metrics with the deep learning model, which is a 38% improvement over the state-of-the-art well known techniques.