ANN based Measurement for NoReference Video Quality of Experience Metric In recent years, there has been a tremendous development in information technology, which has led to the possibility of fast access to different data types over the Internet, one of the data type is video and the ability for the user to watch video directly online. This paper provides a video streaming QoE evaluation metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that are used to train the neural network and finally evaluate the QoE value. Verify training models prediction using 10fold crossvalidation. The proposed system had the best correlation result 0.95 in SRCC metric.

Improving the QoE of OTT Multimedia Services in Wireless Scenarios One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Overthetop (OTT) content is being delivered, as the Internet has now become the xe2x80x98de factoxe2x80x98 path for video transmission, with video data representing over 75% of the entire network traffic, and the usersxe2x80x99 desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clientsxe2x80x99 QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrateadaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.