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

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 b...

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Main Authors: Tho Nguyen Duc, Chanh Minh Tran, Phan Xuan Tan, Eiji Kamioka
Format: Article
Language:English
Published: MDPI AG 2019-08-01
Series:Future Internet
Subjects:
Online Access:https://www.mdpi.com/1999-5903/11/8/171
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spelling doaj-4ddd32eae4874720a8eadbd326c2ebfe2020-11-25T01:07:58ZengMDPI AGFuture Internet1999-59032019-08-0111817110.3390/fi11080171fi11080171Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of InterestTho Nguyen Duc0Chanh Minh Tran1Phan Xuan Tan2Eiji Kamioka3Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, JapanGraduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, JapanSIT Research Laboratories, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, JapanGraduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, JapanThe 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.https://www.mdpi.com/1999-5903/11/8/171quality of experience (QoE)cumulative QoE modelmemory effectdegree of interestvideo-on-demand services
collection DOAJ
language English
format Article
sources DOAJ
author Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
spellingShingle Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
Future Internet
quality of experience (QoE)
cumulative QoE model
memory effect
degree of interest
video-on-demand services
author_facet Tho Nguyen Duc
Chanh Minh Tran
Phan Xuan Tan
Eiji Kamioka
author_sort Tho Nguyen Duc
title Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
title_short Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
title_full Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
title_fullStr Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
title_full_unstemmed Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest
title_sort modeling of cumulative qoe in on-demand video services: role of memory effect and degree of interest
publisher MDPI AG
series Future Internet
issn 1999-5903
publishDate 2019-08-01
description 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.
topic quality of experience (QoE)
cumulative QoE model
memory effect
degree of interest
video-on-demand services
url https://www.mdpi.com/1999-5903/11/8/171
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AT phanxuantan modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest
AT eijikamioka modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest
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