Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

QoE Measurement and Assessment of Video Streaming

  • Xinggong ZhangEmail author
  • Zongming Guo
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_280-1
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Synonyms

Definition

MOS

Mean opinion score

PSNR

Peak signal to noise ratio

QoE

Quality of experience

QoS

Quality of services

SSIM

Structural similarity

History Background

Past few years have witnessed the booms of Internet video. The online video service providers, such as YouTube, Amazon, Hulu from USA and Youku, Tencent, Toutiao from China are becoming the main players in the market of video entertainment (CNNIC 2017; Hossfeld et al. 2011). Mobile phones, over-the-top (OTT) devices, and online streaming are substituting televisions as the new favorable ways for the generation born after 1980. It is necessary for the providers to assess the service quality.

Quality assessment is firstly proposed for digital television, to evaluate the quality of video coding and transmission. Many metrics have been proposed (Seufert et al. 2015), such as the peak signal-to-noise ratio...

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References

  1. CNNIC (2017) The 40th CNNIC statistical survey report on internet development in ChinaGoogle Scholar
  2. Eswara N et al (2018) A continuous QoE evaluation framework for video streaming over HTTP. IEEE Transactions on circuits and systems for video technology 28(11):3236–3250CrossRefGoogle Scholar
  3. Ghadiyaram D et al (2019) A subjective and objective study of stalling events in mobile streaming videos. IEEE Transactions on Circuits and Systems for Video Technology 29(1):183–197CrossRefGoogle Scholar
  4. Hossfeld T et al (2011) Quantification of YouTube QoE via crowdsourcing, 2011 IEEE international symposium on multimedia, Dana Point, 2011, pp. 494–499Google Scholar
  5. ITU-T (2012) Vocabulary for performance and quality of service: recommendation P.10/G.100-Amendment 3Google Scholar
  6. Juluri P et al (2013) Viewing YouTube from a metropolitan area: what do users accessing from residential ISPs experience, 2013 IFIP/IEEE international symposium on integrated network management (IM 2013), Ghent, pp. 589–595Google Scholar
  7. Krishnamoorthi V (2017) BUFFEST: Predicting buffer conditions and real-time requirements of HTTP(S) adaptive streaming clients. Proc ACM MMSys, pp. 76–87Google Scholar
  8. Liu et al (2013) A study on quality of experience for adaptive streaming service, 2013 IEEE International Conference on Communications Workshops (ICC), Budapest, 2013, pp. 682–686Google Scholar
  9. Mangla T (2019) Using session modeling to estimate HTTP-based video QoE metrics from encrypted network traffic. IEEE Transactions on Network and Service Management. 16(3):1086–1099CrossRefGoogle Scholar
  10. Mazhar MH et al (2018) Real-time video quality of experience monitoring for HTTPS and QUIC, IEEE INFOCOM 2018 – IEEE Conference on Computer Communications, Honolulu, 2018, pp. 1331–1339Google Scholar
  11. Seufert M et al (2015) A survey on quality of experience of HTTP adaptive streaming. IEEE Commun Surv Tutorials 17(1):469–492CrossRefGoogle Scholar
  12. Xu Z et al (2019) QoE-driven adaptive K-push for HTTP/2 live streaming. IEEE Transactions on Circuits and Systems for Video Technology 29(6):1781–1794CrossRefGoogle Scholar
  13. Zhang X et al (2012) A control-theoretic approach to rate adaptation for dynamic HTTP streaming, 2012 visual communications and image processing, San Diego, 2012, pp. 1–6Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.WangXuan Institute of Computer TechnologyPeking UniversityBeijingChina

Section editors and affiliations

  • Zhi Liu

There are no affiliations available