Measurement of Quality of Experience of Video-on-Demand Services: A Survey
Abstract
Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services.
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Collected network-level Key Performance Indicators (KPIs) and user-level Key Quality Indicators (KQIs) provide inputs for QoE estimation models.
“The collected network-level Key Performance Indicators (KPIs), e.g., throughput, packet loss, delay or user-level service/application specific Key Quality Indicators (KQIs), e.g., frame rate, video resolution, service usability, and reliability, provide inputs for QoE estimation models [48], [61].”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [48]
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- Authors
- Juluri, P., Tamarapalli, V., & Medhi, D.
- Affiliations
- University of Missouri–Kansas City (US)
- Indian Institute of Technology Guwahati (IN)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2016
- Volume
- 18
- Issue
- 1
- Pages
- 401-418
- ISSN
- 1553-877X (ISSN-L)
- OpenAlex
- W2009005141
- Cited by
- 229
- References
- 79
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 12 · 2023: 18 · 2022: 19 · 2021: 25 · 2020: 35 · 2019: 33 · 2018: 35 · 2017: 22 · 2016: 13 · 2015: 4
- FWCI
- 21.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Social Sciences › Sociology and Political Science › Multimedia Communication and Technology
- Keywords
- quality of experience, video on demand, video streaming, QoE measurement, QoE prediction, user perception, QoE metrics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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