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Streaming of video over wireless heterogeneous networks coping with the problem of packet loss which affects the perceived video quality. The service providers usually use the Peak Signal to Noise Ratio PSNR as a metric measure for the quality of their provided service. So they use the quality of service QoS of the network as a sign on the quality of their presented service. The QoS deal with the objective tests of the provided service, which mean the measure of PSNR of the presented objects. The presented objects may not get the satisfaction of the network users due to many factors although that the PSNR of the used service is enough for presenting the service. Recently the service providers use the Quality of Experience QoE term which deal with the subjective test of the presented object (i.e. the user satisfaction measure). In this paper we propose a new model to identify the importance or the significance of the role of the QoE assessment for the service providers. To verify our proposed model we did a referendum for 55 participants in order to assess their judgment on the quality of some presented videos. The results of the referendum match the consideration of the proposed model.

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Technological development in the last years leads to increase the access speed in the internet networks that allow a huge number of users watching videos online. Video streaming important type in the real-time video sessions and one of the most popular applications in networking systems. The Quality of Service (QoS) techniques give us indicate to the effect of multimedia traffic on the network performance, but this techniques do not reflect the user perception. Using QoS and Quality of Experience (QoE) together can give guarantee to the distribution of video content according to video content characteristics and the user experience . To measure the users’ perception of the quality we use Quality of Experience (QoE) metric . Here , in complete we display what the QoE and QoS mean and what the difference between them, list the techniques used to measured them ; then we display a study of the literature on different tools and measurement methodologies that have been proposed to measure or predict the QoE of video streaming services.

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Technological development in the last years leads to increase the access speed in the networks that allow a huge number of users watching videos online. The Quality of Experience (QoE) Knowledge of services that provide from the network is a very critical matter to have a strong design of multimedia streaming networks. This paper provides a video streaming QoE prediction metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that used to train the neural network and finally prediction the QoE value. Verify models prediction using 10-fold cross-validation that in a regular way split dataset (training set and test set) with multiple percentages. The proposed system verifies the best result.

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