Technological development in recent years leads to increase the access speed in the networks that allow a huge number of users watching videos online. Video streaming is one of the most popular applications in networking systems. Quality of Experience (QoE) measurement for transmitted video streaming may deal with data transmission problems such as packet loss and delay. This may affect video quality and leads to time consuming. We have developed an objective video quality measurement algorithm that uses different features, which affect video quality. The proposed algorithm has been estimated the subjective video quality with suitable accuracy. In this work, a video QoE estimation metric for video streaming services is presented where the proposed metric does not require information on the original video. This work predicts QoE of videos by extracting features. Two types of features have been used, pixel-based features and network-based features. These features have been used to train an Adaptive Neural Fuzzy Inference System (ANFIS) to estimate the video QoE.

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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 impo rtant type in the real-t ime v ideo sessions and one of the most popular applications in networking systems. The Quality of Service (QoS) techniques give us indicate to the effect of mult imedia t raffic 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 (Qo E) metric. Here , in comp lete we display what the QoE and QoS mean and what the difference between them, list the techniques used to measured them ; then we d isplay 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.