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Practical QoE Evaluation of Adaptive Video Streaming

  • Sebastian SurminskiEmail author
  • Christian Moldovan
  • Tobias Hoßfeld
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10740)

Abstract

Video streaming is an increasingly popular service on the Internet. In HTTP adaptive video streaming (HAS), the video is played while being downloaded, and the quality is selected according to the available bandwidth. Due to this, variations in the transmission affect the playback. The quality of the playback can be rated by technical parameters, which can be grouped by the term ‘Quality of Service’ (QoS), like the video quality, the number and duration of stallings or the time until the video starts playing. These metrics differently influence the user experience.

Up to now, no widely accepted model for the Quality of Experience (QoE) for HAS exists. Therefore, we use two conceptually different models and investigate their impact on the resulting QoE. To do so, we use a typical video player, namely the Shaka Player, that can be embedded into websites, and change its buffer configuration. The observed data is then used to evaluate the quality of experience (QoE), combining it into a single ‘Mean opinion score’ (MOS). It can be shown, that, with limitations, these methods can be suited for QoE evaluation.

Keywords

Adaptive video streaming Quality of Experience 

References

  1. 1.
    Brunnström, K., Beker, S.A., De Moor, K., Dooms, A., Egger, S., Garcia, M.N., Hossfeld, T., Jumisko-Pyykkö, S., Keimel, C., Larabi, M.C., et al.: Qualinet white paper on definitions of quality of experience (2013)Google Scholar
  2. 2.
    Chen, L., Zhou, Y., Chiu, D.M.: Video browsing-a study of user behavior in online VoD services. In: 2013 22nd International Conference on Computer Communications and Networks (ICCCN), pp. 1–7. IEEE (2013)Google Scholar
  3. 3.
    Finamore, A., Mellia, M., Munafò, M.M., Torres, R., Rao, S.G.: Youtube everywhere: Impact of device and infrastructure synergies on user experience. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC 2011, pp. 345–360. ACM, New York (2011). http://doi.acm.org/10.1145/2068816.2068849
  4. 4.
    Hoßfeld, T., Seufert, M., Sieber, C., Zinner, T.: Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming. In: 2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 111–116 (2014)Google Scholar
  5. 5.
    Hoßfeld, T., Egger, S., Schatz, R., Fiedler, M., Masuch, K., Lorentzen, C.: Initial delay vs. interruptions: Between the devil and the deep blue sea. In: 2012 Fourth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 1–6. IEEE (2012)Google Scholar
  6. 6.
    Hoßfeld, T., Heegaard, P.E., Varela, M., Möller, S.: QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS. Qual. User Exp. 1(1), 2 (2016)CrossRefGoogle Scholar
  7. 7.
    Hoßfeld, T., Moldovan, C., Schwartz, C.: To each according to his needs: Dimensioning video buffer for specific user profiles and behavior. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 1249–1254. IEEE (2015)Google Scholar
  8. 8.
    Hoßfeld, T., Skorin-Kapov, L., Heegaard, P.E., Varela, M., Chen, K.T.: On additive and multiplicative QoS-QoE models for multiple QoS parameters. In: Proceedings of the 5th ISCA/DEGA Workshop on Perceptual Quality of Systems PQS 2016. ISCA (2016)Google Scholar
  9. 9.
    Kara, P.A., Bokor, L., Sackl, A., Mourão, M.: What your phone makes you see: Investigation of the effect of end-user devices on the assessment of perceived multimedia quality. In: 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), pp. 1–6 (2015)Google Scholar
  10. 10.
    International Telecommunication Union: P.800: Methods for subjective determination of transmission quality (1996)Google Scholar
  11. 11.
    Müller, C., Lederer, S., Timmerer, C.: An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. In: Proceedings of the 4th Workshop on Mobile Video, MoVid 2012, pp. 37–42. ACM, New York (2012). http://doi.acm.org/10.1145/2151677.2151686
  12. 12.
    Riiser, H., Vigmostad, P., Griwodz, C., Halvorsen, P.: Commute path bandwidth traces from 3G networks: Analysis and applications. In: Proceedings of the 4th ACM Multimedia Systems Conference, MMSys 2013, pp. 114–118. ACM, New York (2013). http://doi.acm.org/10.1145/2483977.2483991
  13. 13.
    Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., Tran-Gia, P.: A survey on quality of experience of HTTP adaptive streaming. IEEE Commun. Surv. Tutorials 17(1), 469–492 (2015)CrossRefGoogle Scholar
  14. 14.
    Song, W., Tjondronegoro, D.W.: Acceptability-based QoE models for mobile video. IEEE Trans. Multimedia 16(3), 738–750 (2014)CrossRefGoogle Scholar
  15. 15.
    Surminski, S., Moldovan, C., Hoßfeld, T.: Saving bandwidth by limiting the buffer size in HTTP adaptive streaming. In: MMBnet 2017 - Proceedings of the 9th GI/ITG Workshop “Leistungs-, Verlässlichkeits- und Zuverlässigkeitsbewertung von Kommunikationsnetzen und Verteilten Systemen”, pp. 5–21 (2017)Google Scholar
  16. 16.
    Wamser, F., Casas, P., Seufert, M., Moldovan, C., Tran-Gia, P., Hossfeld, T.: Modeling the youtube stack: From packets to quality of experience. Comput. Netw. 109(Part 2), 211–224 (2016). http://www.sciencedirect.com/science/article/pii/S1389128616300925

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sebastian Surminski
    • 1
    Email author
  • Christian Moldovan
    • 1
  • Tobias Hoßfeld
    • 1
  1. 1.Chair of Modeling of Adaptive SystemsUniversity of Duisburg-EssenEssenGermany

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