  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.
