• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2513-2530
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• 2021

With the fast development of information and communication technologies, video streaming services and applications are increasing rapidly. However, the network condition is volatile. In order to provide users with better quality of service, it is necessary to develop an accurate and low-complexity model for Quality of Experience (QoE) prediction of time-varying video. Memory effects refer to the psychological influence factor of historical experience, which can be taken into account to improve the accuracy of QoE evaluation. In this paper, we design subjective experiments to explore the impact of Short-Term Memory (STM) on QoE. The experimental results show that the user's real-time QoE is influenced by the duration of previous viewing experience and the expectations generated by STM. Furthermore, we propose analytical models to determine the relationship between intrinsic video quality, expectation and real-time QoE. The proposed models have better performance for real-time QoE prediction when the video is transmitted in a fluctuate network. The models are capable of providing more accurate guidance for improving the quality of video streaming services.