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

Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan and Eiji Kamioka
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Tho Nguyen Duc: Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan
Chanh Minh Tran: Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan
Phan Xuan Tan: SIT Research Laboratories, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan
Eiji Kamioka: Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan

Future Internet, 2019, vol. 11, issue 8, 1-18

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.

Keywords: quality of experience (QoE); cumulative QoE model; memory effect; degree of interest; video-on-demand services (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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