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Article
Quality of Experience Metric of Streaming Video: A survey

Authors: Rana Fareed Ghani --- Amal Sufiuh Ajrash
Journal: Iraqi Journal of Science المجلة العراقية للعلوم ISSN: 00672904/23121637 Year: 2018 Volume: 59 Issue: 3B Pages: 1531-1537
Publisher: Baghdad University جامعة بغداد

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Abstract

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.


Article
A Proposed Measurement for Video Quality of Experience

Authors: Rana Fareed Ghani --- Amal Sufiuh Ajrash
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2019 Volume: 22 Issue: 3 Pages: 75-81
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

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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