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3.2.4 QoE estimation QoE is defined by ITU-T as “the overall acceptability of an application or service, as perceived subjectively by the end user”. The ability to evaluate the QoE in a communication system, and especially in a system involved in video transmission, is critical. One of the main objectives of modern network management systems is to monitor and guarantee end-user Quality of Experience (QoE), hence the importance of an accurate QoE monitoring system. The usual way to evaluate QoE is either to carry out experiments with individuals as testers or to calculate it indirectly from Quality of Service (QoS) network parameters (jitter, delay, packet loss....) [42][114][115]. Another approach, recently being actively explored is applying machine learning (ML) to video QoE estimation. The resulting QoE detector must predict a QoE score directly from information contained in the transmitted videos, the network packets or end-user recorded events (e.g. related web activity). This approach is the one taken for the research performed as part of this thesis which provides a video QoE detector from network packets information using deep learning models. This is also the most advanced and precise approach [42] that shifts the focus of video quality assessment from QoS (system oriented) to QoE (user oriented). The datasets used to perform experiments in QoE are mainly proprietary, as has been the case for the dataset used for the research carried out for this thesis. As far as we know, there are no previous works presenting the application of a CNN+RNN model to video QoE estimation, hence we believe that the research presented in this thesis [4] is original in this regard. The following table presents a summary of the main works related to the research carried out for this thesis. It provides a reference to the document, the data set used and the scope of the work. Objective/Area Ref. Dataset Scope QoE estimation [39] System prototype in an OpenStack based virtualization environment -They design a three-tier edge computing system architecture to elastically adjust computing capacity and dynamically route data to proper edge servers for real-time surveillance applications. - It demonstrates the reconfiguration capabilities of current network services. [40] N/A - This work highlights some of the potentials and prospects of edge computing for interactive media. -It presents the importance of QoE estimate and control in multimedia applications [41] N/A - It provides an overview of real-time video analytics applications that are (or will soon be) performed by Doctoral Thesis: Novel applications of Machine Learning to NTAP - 35PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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