{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T09:47:20Z","timestamp":1772790440072,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,4]],"date-time":"2019-08-04T00:00:00Z","timestamp":1564876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2019s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.<\/jats:p>","DOI":"10.3390\/fi11080171","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T03:25:22Z","timestamp":1564975522000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest"],"prefix":"10.3390","volume":"11","author":[{"given":"Tho","family":"Nguyen Duc","sequence":"first","affiliation":[{"name":"Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan"}]},{"given":"Chanh","family":"Minh Tran","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9592-0226","authenticated-orcid":false,"given":"Phan Xuan","family":"Tan","sequence":"additional","affiliation":[{"name":"SIT Research Laboratories, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan"}]},{"given":"Eiji","family":"Kamioka","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering and Science, Shibaura Institute of Technology, 3 Chome-7-5 Toyosu, Koto City, Tokyo 135-8548, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30831","DOI":"10.1109\/ACCESS.2019.2901778","article-title":"QoE Modeling for HTTP Adaptive Video Streaming-A Survey and Open Challenges","volume":"7","author":"Barman","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tran, H.T.T., Ngoc, N.P., Ho\u00dffeld, T., and Thang, T.C. 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