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models. This is also the most advanced and precise approach [6] that shifts the focus of video quality assessment from QoS (system oriented) to QoE (user oriented). Building a QoE detector raises important challenges. First, it is difficult to construct a training dataset, since it is obtained in a controlled experiment with several individuals who have to evaluate the quality of the video. This makes it very difficult to acquire large datasets, which are normally needed to train a classification algorithm. Secondly, the training datasets are highly unbalanced, as the number of errors observed in the videos is normally much smaller than the number of non-anomaly events. And third, the subjective judgment of quality, assumed by QoE, necessarily implies noisy results (even using Mean Opinion Score (MOS)), which can make it even more difficult to assess the performance of the algorithms. Additionally to all former considerations, other important objective in our case was to have a QoE detector which could be integrated into a network management system to monitor network quality (as observed by the end-user), allowing at the same time an efficient network reconfiguration and control (in our case an SDN network). Therefore, QoE detector could identify the QoE score of the video transmitted at the current time-interval, but also be able to anticipate (predict) the quality score for the next time-interval. Since, this prediction can be crucial to anticipate actions on network resources. Having in mind these challenges, the proposed QoE detector consists of a deep learning classifier that is based on the combination of a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) with a final Gaussian Process (GP) classifier. The classifier implements a binary classification (good or bad quality) for seven usual classes of video anomalies (blur, ghost, columns, chrominance, blockness, color bleeding and black pixel [6]) that can happen when watching the videos. In order to design the final model, we have tried several alternative architectures and different ML models. We present a complete analysis of the results obtained from these alternative models. The impact of several algorithms’ hyper-parameters and design decisions has also been analyzed. Similarly, the process for generating and transforming the training data is presented in detail. QoE detector utilizes a training dataset created specifically for this work. The dataset was obtained from a controlled experiment in which several individual viewers evaluated video transmissions in a time interval of 1-second and under different network configurations. The main contributions of this work are: (1) First application of deep learning models to video QoE prediction. (2) Prediction based on network packet information. (3) Network flows treated as pseudo-images that allow applying a CNN. (4) Excellent prediction performance for not extremely unbalanced labels with a small dataset. The structure of the paper is the following: Related work is presented in Section II. The work performed is described in Section III. The results are discussed in Section IV and finally, Section V provides discussion and conclusions. II. RELATED WORK There is no similar work in the literature presenting a deep learning solution to video QoE assessment based on information contained in network packets and trained with end-user QoE evaluations in a controlled experiment. Nevertheless, there is a solid work done on automatic Video Quality Assessment (VQA) based on the identification and processing of parameters extracted from the video. In [6] a thorough review of QoE modeling and methodologies is presented. Authors in [4,5] propose an analytical expression for video QoE calculation based on several parameters: jitter, delay, Doctoral Thesis: Novel applications of Machine Learning to NTAP - 149PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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