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Open AccessArticle

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

1
Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan
2
SIT Research Laboratories, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan
*
Authors to whom correspondence should be addressed.
Future Internet 2019, 11(8), 171; https://doi.org/10.3390/fi11080171
Received: 29 June 2019 / Revised: 2 August 2019 / Accepted: 2 August 2019 / Published: 4 August 2019
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. View Full-Text
Keywords: quality of experience (QoE); cumulative QoE model; memory effect; degree of interest; video-on-demand services quality of experience (QoE); cumulative QoE model; memory effect; degree of interest; video-on-demand services
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Nguyen Duc, T.; Minh Tran, C.; Tan, P.X.; Kamioka, E. Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest. Future Internet 2019, 11, 171.

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