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bandwidth, loss packets and zapping time for IPTV video transmissions. There is also relevant literature on ML models that are applied to features extracted from the videos (or network packets) in order to rank its quality, usually in accordance with quality assessments obtained from end-users. In this line, in [7] they use QoS parameters to predict QoE using a dataset built from subjective end-user scores, and applying machine learning algorithms based on Support Vector Machine (SVM) and Decision Trees. A survey of ML techniques used to capture the relationship between QoS parameters and QoE scores is provided in [8], where most of the common machine learning algorithms (Linear Discriminant Analysis, Random Forest (RF), SVM, Naïve Bayes, K-Nearest Neighbors) are applied to the automatic identification of QoE from QoS network parameters. Considering Content Delivery Networks (CDN), [9] gives a review of the reasons why developing an objective method of quality assessment based on video transmission parameters is extremely difficult due to the complex relationships between these parameters, the user’s perception and even the nature of the content. Furthermore, the authors propose the application of ML algorithms (Decision Trees, Naïve Bayes and Logistic Regression) to predict the QoE based on transmission parameters (bitrates, latency,..) and end-user engagement attributes (playtime, number of visits,..). The prediction of streaming video QoE is proposed in [10] applying several regression models such as Ridge and Lasso Regression, and ensemble methods such as Random Forest (RF), Gradient Boosting (GB) and Extra Trees (ET). None of the above references apply the new deep learning models and they do not provide a short-term QoE prediction based on network packet information. The QoE score generally provided is a single score in contrast to the simultaneous prediction of seven QoE anomalies/errors, which is provided in this paper. Comparison of performance results between these works is not significant, since the datasets used and the areas of application are too different. In this context, the present work has to be considered as an alternative option available in this topic area. III. WORK DESCRIPTION This section presents the experimental configuration employed to generate the training data, the necessary data preparation and a description and comparison of the prediction models applied. A. Experimental setup: data generation To generate the data that the QoE prediction models will use, it was necessary to establish an experimental setup that would allow identifying the QoE of the video transmissions while recording the associated network flow packets. The resulting data are multivariate time series, in time-steps of 1-second, which contain the network packets transmitted in each time-step plus the presence or absence of seven video transmission errors in that time-step. The topology of the experimental setup included three components: (1) A video transmission server, which allowed us to vary the characteristics of the video. (2) The clients, where the video streams are visualized by the end user to label them with QoE errors. And, (3) a packet analyzer (Wireshark) that extracts the network parameters on the end user's side. This configuration allows us to vary several network and video features (jitter, delay, bandwidth, packet loss, bitrate ...) and test their impact on network packets and their associated visual Doctoral Thesis: Novel applications of Machine Learning to NTAP - 150PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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