![]() ![]() The automated model could function to demonstrate the importance of correlation for network behaviors’ parameters. The experiment’s evaluation demonstrated that objective metrics can be mapped to the most significant subjective parameters for user’s experience. Besides, some parameters are considered such as video characteristics, segment length, initial delay, switch strategy, stalls, as well as QoS parameters. This paper introduces a methodology design for the evaluation of subjective QoE in adaptive video streaming over wireless networks. It can also cause a challenging behavior regarding subjective and objective QoE evaluations of HTTP adaptive video over time since each Quality of Service (QoS) parameter affects the QoE of end-users separately. However, such a technology has increased the demand for Quality of Experience (QoE) in terms of prediction and assessment. In adaptive technology services, a client’s application receives a streaming video through the adaptation of its quality to the network condition. Furthermore, HTTP adaptive video streaming (HAS) technology transmits the video data over wired and wireless networks. Nowadays, heterogeneous devices are widely utilizing Hypertext Transfer Protocol (HTTP) to transfer the data.
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