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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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 |
work_keys_str_mv |
AT thonguyenduc modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest AT chanhminhtran modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest AT phanxuantan modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest AT eijikamioka modelingofcumulativeqoeinondemandvideoservicesroleofmemoryeffectanddegreeofinterest |
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