Modeling of cumulative QoE in on-demand video services: Role of memory effect and degree of interest

Tho Nguyen Duc, Chanh Minh Tran, Tan Phan Xuan, Eiji Kamioka

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number171
JournalFuture Internet
Volume11
Issue number8
DOIs
Publication statusPublished - 2019 Aug 1

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Video on demand
Data storage equipment
Video streaming

Keywords

  • Cumulative QoE model
  • Degree of interest
  • Memory effect
  • Quality of experience (QoE)
  • Video-on-demand services

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Modeling of cumulative QoE in on-demand video services : Role of memory effect and degree of interest. / Duc, Tho Nguyen; Tran, Chanh Minh; Phan Xuan, Tan; Kamioka, Eiji.

In: Future Internet, Vol. 11, No. 8, 171, 01.08.2019.

Research output: Contribution to journalArticle

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