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In data service applications over the Internet, user perception and satisfaction can be assessed by quality of experience (QoE) metrics. QoE depends both on the users' perception and the used service, which together form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, we focus in this work on optimizing end-to-end QoE metrics with the aim to deliver to the client a good QoE that can be monitored in real time. We argue that end-user QoE is a relevant measurement for network operators and service providers. In this paper, we present a machine learning approach combined with adaptive video delivery service in order to provide a better QoE for video streaming services. This solution will be established using SDN architecture. The first part of the paper deals with a brief introduction of SDN networks, QoE requirement, and ML algorithms. Secondly, we expose the rating of the web application that we developed. This will help in conducting a subjective study to collect MOS on real-time as well as objective parameters SSIM, VQM, and PSNR. At the end, we expose our QoE-aware monitoring approach and explain what it is based on. [ABSTRACT FROM AUTHOR]

In real time video streaming, the frames must meet their timing constraints, typically specified as their deadlines. Wireless networks may suffer from bandwidth limitations. To reduce the data transmission over the wireless networks, we propose an adaption technique in the server side by extracting a part of the video frames that considered as a Region Of Interest (ROI), and drop the part outside the ROI from the frames that are between reference frames. The estimated position of the selection of the ROI is computed by using the Sum of Squared Differences (SSD) between consecutive frames. The reconstruction mechanism to the region outside the ROI is implemented in the mobile side by using linear interpolation between reference frames. We evaluate the proposed approach by using Mean Opinion Score (MOS) measurements. MOS are used to evaluate two scenarios with equivalent encoding size, where the users observe the first scenario with low bit rate for the original videos, while for the second scenario the users observe our proposed approach with high bit rate. The results show that our technique significantly reduces the amounts of data are streamed over wireless networks, while the reconstruction mechanism will provides acceptable video quality. [ABSTRACT FROM AUTHOR]