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V . CONCLUSION This work shows that it is possible to apply new deep learning models whose origin focused mainly on the areas of video, audio and language processing for the prediction of QoE of transmitted videos. We have extended the work in [15], demonstrating that a CNN can be applied to a time-series of samples (formed by aggregated information from network packets) to predict QoE for video transmission. As far as we know, there is no previous application of a deep learning model to QoE prediction, being this work the first contribution to this area. The proposed model can be integrated into a network management system to monitor network quality (as observed by the end-user), which is an essential part of a self-adapting network (e.g. SDN, edge computing...). The model is applicable to a real-time environment (in time-steps of 1-second) and is able to predict video QoE for current and near-future video transmissions. The best proposed model includes a combination of CNN and RNN networks, being the CNN network the most critical piece. We have extended the study with the inclusion of a GP Classifier that, being a non-parametric model, could make full use of the scarce data available. This inclusion provides a slight improvement in the prediction results, but has to be considered in parallel with the increase in memory and processing time required. In future work, we plan to investigate the application of generative models (e.g. variational autoencoders) to create synthetic data and explore one-shot learning advances. ACKNOWLEDGMENTS This work has been funded by the Ministerio de Economía y Competitividad del Gobierno de España and the Fondo de Desarrollo Regional (FEDER) within the project "Inteligencia distribuida para el control y adaptación de redes dinámicas definidas por software, Ref: TIN2014-57991-C3-2-P", and, also by the Ministerio de Economía y Competitividad in the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento with the projects “Distribucion inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software, Ref: TIN2014-57991-C3-1-P" and “Red Cognitiva Definida por Software Para Optimizar y Securizar Tráfico de Internet de las Cosas con Informacion Critica”, Ref TIN2017-84802-C2-1-P. REFERENCES [1] J. Wang, J. Pan, and F. Esposito, “Elastic urban video surveillance system using edge computing,” Proceedings of the Workshop on Smart Internet of Things (SmartIoT '17). ACM, New York, NY, USA, 2017, Article 7 [2] K. Bilal, A. Erbad, "Edge computing for interactive media and video streaming," 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), Valencia, Spain, May 8-11, 2017, pp. 68-73 [3] G. Ananthanarayanan et al., "Real-Time Video Analytics: The Killer App for Edge Computing," Computer, vol. 50, no. 10, 2017, pp. 58-67. [4] J. Lloret et al., “A QoE management system to improve the IPTV network,” International Journal of Communication Systems, 24, 2011, pp. 118–138. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 158PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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