Abstract
The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.
Subject
Computer Networks and Communications
Cited by
9 articles.
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1. Memory-Effect Based QoE Evaluation Method and Guarantee Scheme in APN-Driven Game Acceleration;IEEE Transactions on Network Science and Engineering;2024-09
2. Quality Assessment of Video Services in the Long Term;Proceedings of the 2023 ACM International Conference on Interactive Media Experiences;2023-06-12
3. Sprinkle Prebuffer Strategy to Improve Quality of Experience with Less Data Wastage in Short-Form Video Streaming;Electronics;2022-06-22
4. EyeQoE;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2022-03-29
5. Modeling of Cognitive Bias of Video Viewing Users Based on Quantum Decision Making;2022 International Conference on Information Networking (ICOIN);2022-01-12