Quality of Experience-Centric Management of Adaptive Video Streaming Services: Status and Challenges
Open accessAbstract
Video streaming applications currently dominate Internet traffic. Particularly, HTTP Adaptive Streaming (HAS) has emerged as the dominant standard for streaming videos over the best-effort Internet, thanks to its capability of matching the video quality to the available network resources. In HAS, the video client is equipped with a heuristic that dynamically decides the most suitable quality to stream the content, based on information such as the perceived network bandwidth or the video player buffer status. The goal of this heuristic is to optimize the quality as perceived by the user, the so-called Quality of Experience (QoE). Despite the many advantages brought by the adaptive streaming principle, optimizing users’ QoE is far from trivial. Current heuristics are still suboptimal when sudden bandwidth drops occur, especially in wireless environments, thus leading to freezes in the video playout, the main factor influencing users’ QoE. This issue is aggravated in case of live events, where the player buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In light of the above, in recent years, several works have been proposed with the aim of extending the classical purely client-based structure of adaptive video streaming, in order to fully optimize users’ QoE. In this article, a survey is presented of research works on this topic together with a classification based on where the optimization takes place. This classification goes beyond client-based heuristics to investigate the usage of server- and network-assisted architectures and of new application and transport layer protocols. In addition, we outline the major challenges currently arising in the field of multimedia delivery, which are going to be of extreme relevance in future years.
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Existing survey papers on QoE management do not explain how QoE can be improved or optimized using SDN and NFV.
“However, the survey papers mentioned above do not explain how QoE can be improved or optimized using SDN and NFV.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [31]
Existing studies do not provide insights into how network or service providers can utilize the capabilities of SDN and NFV for QoE management to meet end users' quality demands.
“As such, these studies do not provide insights into how network or service providers can utilize the capabilities of SDN and NFV for QoE management (e.g., to achieve automation, programmability, flexibility, scalability and network control) to meet the end users' quality demands.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [31]
The user in VR is immersed in a virtual environment and can dynamically and freely decide the preferred viewpoint.
“The user in VR is immersed in a virtual environment and can dynamically and freely decide the preferred viewpoint [31].”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [31]
The current QoE models for delivery of adaptive video streams have three limitations: they are developed to capture the behavior of the average user and hence some are not personalized; they do not consider the context in which the streaming session takes place; and only the QoE model of the users is inserted into the control loop, but not the user herself.
“The current QoE models for delivery of adaptive video streams have three limitations: (1) they are developed to capture the behavior of the”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [31]
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Song, M., Lee, Y., & Park, J.-H. (2015) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2700282 · Cited by 22 · see citing works
The cost of aggressive HTTP adaptive streaming: Quantifying YouTube's redundant traffic
Sieber, C., Blenk, A., Hinteregger, M., et al. (2015) · IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.1109/inm.2015.7140478 · Cited by 20 · see citing works
Bandwidth-aware Prefetching for Proactive Multi-video Preloading and Improved HAS Performance
Krishnamoorthi, V., Carlsson, N. F., Eager, D. L., Mahanti, A., & Shahmehri, N. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806270 · Cited by 19 · see citing works
2023 19th International Conference on Network and Service Management (CNSM)
Boutaba, R., Chemouil, P., Limam, N., et al. (2023) · doi:10.23919/cnsm59352.2023 · Cited by 16 · see citing works
QoE-Based SVC Layer Dropping in LTE Networks Using Content-Aware Layer Priorities
Fu, B., Staehle, D., Kunzmann, G., et al. (2015) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2754167 · Cited by 14 · see citing works
YouTube in the move: Understanding the performance of YouTube in cellular networks
Casas, P., Fiadino, P., Sackl, A., et al. (2014) · IFIP Wireless Days · doi:10.1109/wd.2014.7020798 · Cited by 14 · see citing works
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2006 IEEE International Conference on Multimedia and Expo (ICME)
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A Generic Utility Model Representing the Quality of Sensory Experience
Rainer, B., & Timmerer, C. (2014) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2648429 · Cited by 12 · see citing works
Cooperative Announcement-Based Caching for Video-on-Demand Streaming
Claeys, M., Bouten, N., Vleeschauwer, D. D., et al. (2016) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2016.2546459 · Cited by 12 · see citing works
Advanced Transport Options for the Dynamic Adaptive Streaming over HTTPPreprint
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QoE Driven Server Selection for VoD in the Cloud
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Flagged first, then by citations
Research works printed but not linked: 4
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Cited by
Sorted by citations
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239 · see citing works
- Authors
- Petrangeli, S., Hooft, J. V. D., Wauters, T., & Turck, F. D.
- Affiliations
- Ghent University (BE)
- Venue
- ACM Transactions on Multimedia Computing, Communications, and Applications
- Published
- 2018-04-30
- Volume
- 14
- Issue
- 2s
- Pages
- 1-29
- ISSN
- 1551-6857 (ISSN-L)
- 1551-6865
- OpenAlex
- W2801125139
- Cited by
- 38
- References
- 117
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 3 · 2023: 3 · 2022: 7 · 2021: 9 · 2020: 4 · 2019: 6
- FWCI
- 2.38
- Type
- journal article
- Language
- en
- Open access
- green
- biblio.ugent.be
- Locations
- ACM Transactions on Multimedia Computing Communications and Applications (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- Agency for Innovation by Science and Technology in Flanders
- 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
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Keywords
- adaptive video streaming, HTTP adaptive streaming, quality of experience, transport protocols, live events, multimedia transmission
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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