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7.4 Paper 4: Deep learning model for multimedia Quality of Experience prediction based on network flow packets Manuel Lopez-Martin, Belen Carro, Jaime Lloret, Santiago Egea, Antonio Sanchez-Esguevillas Deep learning model for multimedia Quality of Experience prediction based on network flow packets 10.435 Q1 1 (https://scholar.google.es/citations?user=3RSZbOYAAAAJ&hl=es) Published, September 2018 https://doi.org/10.1109/MCOM.2018.1701156 7.4.1 Objectives The objective of this work was to build a video QoE detector/predictor using information extracted from network packets. The intention was also to explore the use of deep learning models to achieve the goal. Other important objective was to have a QoE classifier 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, the QoE classifier needed to 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. The resulting QoE classifier must implement a binary classification (good or bad quality) for seven usual classes of video anomalies (blur, ghost, columns, chrominance, blockness, color bleeding and black pixel) that can happen when watching the videos. 7.4.2 Datasets For this work we have created our own dataset of video QoE scores provided by individuals watching video streams under different network conditions. To obtain this dataset we created a specific experimental setup. 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 Authors Title Journal IEEE Communications Magazine, vol. 56, no. 9, pp. 110-117, Sept. 2018 Feature Topic: Enabling Technologies for Smart Internet of Things Impact Factor Quartile #Citations Status Link Doctoral Thesis: Novel applications of Machine Learning to NTAP - 73PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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