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Cited by 844Image and Video Quality AssessmentNetwork Traffic and Congestion ControlMultimedia Communication and Technology

A Survey on Quality of Experience of HTTP Adaptive Streaming

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Seufert, Michael, Egger, Sebastian, Slanina, Martin, Zinner, Thomas, Hoßfeld, Tobias, Tran-Gia, Phuoc·2015·IEEE Communications Surveys & Tutorials
DOI 10.1109/comst.2014.2360940
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Abstract

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

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  • The proposed crypto-transcoder has advantages over previously proposed schemes in [15], [54] and [58], though to some extent these advantages are more to do with the development of the HEVC codec than merits of prior schemes.

    “The crypto-transcoder has advantages over previously proposed schemes in [15], [54] and [58], though to some extent these are more to do with the development of the HEVC codec than merits of prior schemes.”

    From Effectiveness of crypto-transcoding for H.264/AVC and HEVC video bit-streams (2019) · cites this paper as [58]

    Section 3.3 (Server-side Actions; page unavailable) describes HEVC as an efficient codec, but does not describe a crypto-transcoding scheme or compare one with the proposed crypto-transcoder. Searches for crypto-transcoding and encrypted-video transcoding found no passage addressing that comparison. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • When H.264/AVC is targeted, the computational overhead in terms of encryption with transcoding time appears to be lower than the schemes proposed in [15], [54], and [58].

    “Meanwhile, when H.264/AVC is targeted, the computational overhead in terms of encryption with transcoding time appears to be lower than the schemes proposed in [15], [54], and [58].”

    From Effectiveness of crypto-transcoding for H.264/AVC and HEVC video bit-streams (2019) · cites this paper as [58]

    Section 3.3 discusses H.264/AVC and the file-size overhead of scalable video coding, but it does not compare encryption or transcoding times. Searches for encryption, transcoding, and computational overhead found no passage supporting the claimed comparison with schemes [15], [54], and [58]. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The majority of Internet video traffic today is delivered via HAS.

    “The majority of Internet video traffic today is delivered via HAS [6].”

    From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [6]

  • Many studies have focused on enhancing QoE by optimizing video quality adaptation algorithms on the client and/or server-side.

    “Many studies have been focusing on enhancing the QoE by optimizing the video quality adaptation algorithms on the client and/or the server-side [6], [191].”

    From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [6]

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Authors
Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P.
Affiliations
  • University of Würzburg (DE)
  • Forschungszentrum Telekommunikation Wien (AT)
  • Brno University of Technology (CZ)
  • Inst. of Comput. Sci., Univ. of Wurzburg, Wurzburg, Germany
ORCID
Venue
IEEE Communications Surveys & Tutorials
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015
Volume
17
Issue
1
Pages
469-492
ISSN
  • 1553-877X (ISSN-L)
  • 2373-745X
DOI
10.1109/comst.2014.2360940
OpenAlex
W1986570368
Cited by
844
References
2
Citations by year
2026: 40 · 2025: 33 · 2024: 36 · 2023: 43 · 2022: 47 · 2021: 57 · 2020: 90 · 2019: 120 · 2018: 132 · 2017: 114 · 2016: 88 · 2015: 44
FWCI
73.33
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Communications Surveys & Tutorials (publishedVersion)
  • OPUS (Augsburg University) (submittedVersion)
Funders
  • COST Action IC1003 Qualinet
  • Deutsche Forschungsgemeinschaft (HO4770/2-1, TR257/38-1)
  • Brno University of Technology (CZ.1.07/2.3.00/30.0005)
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
HTTP adaptive streaming, quality of experience, video streaming, QoE model, bandwidth utilization, server load

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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