MiSAL - A minimal quality representation switch logic for adaptive streaming

  • Amit DvirEmail author
  • Nissim Harel
  • Ran Dubin
  • Refael Barkan
  • Raffael Shalala
  • Ofer Hadar


Quality of Experience is affected by many parameters. For this reason, client-side adaptation logic algorithms often adopt the strategy of optimizing a subset of parameters in the hope of improving the overall QoE. However, as shown here, this approach ends up degrading parameters that are crucial to good Quality of Experience. To resolve this conundrum, we present a new approach for improved Quality of Experience dubbed: Minimal Switch AL (MiSAL). This algorithm substantially reduces the number of quality level switches by monitoring the client buffer level and carefully estimating the channel bandwidth and the Round Trip Time. A comparison of MiSAL against leading ALs demonstrates that this approach successfully in optimizes several important parameters that affect Quality of Experience without negatively affecting other parameters. It is shown that MiSAL can provide a close to optimal QoE under many different network conditions.


DASH Adaptation logic Quality of experience 



This research was partially supported by the Israeli NET-HD consortium. The authors wish to thank Ofir Ahrak for his helpful discussions and advice.


  1. 1.
    Akhshabi S, Begen AC, Dovrolis C (2011) An experimental evaluation of rate-adaptation algorithms in adaptive streaming over http. In: ACM Multimedia systems, CA, pp 157–168Google Scholar
  2. 2.
    Brunnström K, Beker SA, De Moor K, Dooms A, Egger S, Garcia M-N, Hoßfeld T, Jumisko-pyykkö S, Keimel C, Larabi M-C et al (2013) Qualinet white paper on definitions of quality of experience The open archive HALGoogle Scholar
  3. 3.
    Cisco (2017) Cisco visual networking index: Global mobile data traffic forecast update, 2016-2021. Technical report, CiscoGoogle Scholar
  4. 4.
    Claeys M, Latré S, Famaey J, Wu T, Van Leekwijck W, De Turck F (2014) Design and optimisation of a (fa) q-learning-based http adaptive streaming client. Connect Sci 26(1):25–43CrossRefGoogle Scholar
  5. 5.
    Colonnese S, Cuomo F, Melodia T, Guida R (2013) Cloud-assisted buffer management for http-based mobilevideo streaming. In: Proceedings of the 10th ACM symposium on Performance evaluation of wireless ad hoc, sensor, & ubiquitous networks. ACM, pp 1–8Google Scholar
  6. 6.
    Cranley N, Perry P, Murphy L (2006) User perception of adapting video quality. Int J Human-Comput Stud 64(8):637–647CrossRefGoogle Scholar
  7. 7.
    De Cicco L, Caldaralo V, Palmisano V, Mascolo S (2013) Elastic: a client-side controller for dynamic adaptive streaming over http (dash). In: Packet video workshop (PV). IEEE, pp 1–8Google Scholar
  8. 8.
    De Vriendt J, De Vleeschauwer D, Robinson D (2013) Model for estimating qoe of video delivered using http adaptive streaming. In: 2013 IFIP/IEEE international symposium on Integrated network management (IM 2013). IEEE, pp 1288–1293Google Scholar
  9. 9.
    Dubin R, Hadar O, Dvir A (2013) The effect of client buffer and mbr consideration on dash adaptation logic. In: WCNC, pp 2178–2183Google Scholar
  10. 10.
    Dubin R, Dvir A, Pele O, Hadar O, Katz I, Mashiach O (2018) Adaptation logic for http dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users. Multimed Syst 24(1):19–31CrossRefGoogle Scholar
  11. 11.
    Garcia M-N, De Simone F, Tavakoli S, Staelens N, Egger S, Brunnström K, Raake A (2014) Quality of experience and http adaptive streaming: a review of subjective studies. In: 6E international workshop on quality of multimedia experience, proceedings, pp 1–6Google Scholar
  12. 12.
    Grafl M, Timmerer C (2013) Representation switch smoothing for adaptive http streaming. In: Proceedings of the 4th International Workshop on Perceptual Quality of Systems (PQS)Google Scholar
  13. 13.
    Hoßfeld T, Seufert M, Sieber C, Zinner T (2014) Assessing effect sizes of influence factors towards a qoe model for http adaptive streaming. In: 6Th international workshop on quality of multimedia experience (qoMEX). IEEE, pp 9Google Scholar
  14. 14.
    Hoßfeld T, Seufert M, Sieber C, Zinner T, Tran-Gia P (2015) Identifying qoe optimal adaptation of http adaptive streaming based on subjective studies. Comput. Netw. 81:320–332CrossRefGoogle Scholar
  15. 15.
    ISO/IEC (2014) Information technology - Dynamic adaptive streaming over HTTP (DASH). Technical report, ISO, USAGoogle Scholar
  16. 16.
    ITU (2014) Methods for the subjective assessment of video quality, audio quality and audiovisual quality of internet video and distribution quality television in any environmentGoogle Scholar
  17. 17.
    Jiang J, Sekar V, Zhang H (2012) Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. In: CoNEXT. ACM, pp 97–108Google Scholar
  18. 18.
    Jumisko-Pyykkö S, Häkkinen J, Nyman G (2007) Experienced quality factors: qualitative evaluation approach to audiovisual quality. In: Electronic Imaging 2007, pages 65070M–65070M. International Society for Optics and PhotonicsGoogle Scholar
  19. 19.
    Li Y, Zhou G, Zheng N, Hong L (2014) An adaptive backoff algorithm for multi-channel CSMA in wireless sensor networks. Neural Comput Appl 25(7–8):1845–1851CrossRefGoogle Scholar
  20. 20.
    Li Y, Du S, Zhou G (2017) Energy optimization for mobile video streaming via an aggregate model. Multimed Tools Appl 76(20):20781–20797CrossRefGoogle Scholar
  21. 21.
    Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. In: ACM Multimedia Systems, CA, pp 169–174Google Scholar
  22. 22.
    Mok R, Chan E, Chang R (2011) Measuring the quality of experience of http video streaming. In: IEEE/IFIP Integrated network management, Dublin, pp 1–8Google Scholar
  23. 23.
    Mok RKP, Chan EWW, Luo X, Chang RKC (2012) Qdash: a qoe-aware dash system. In: Multimedia systems conference. ACM, pp 11–22Google Scholar
  24. 24.
    Moorthy AK, Choi LK, Bovik AC, De Veciana G (2012) Video quality assessment on mobile devices Subjective, behavioral and objective studies. IEEE J Sel Top Signal Process 6(6):652–671CrossRefGoogle Scholar
  25. 25.
    Mueller C, Lederer S, Timmerer C (2012) A proxy effect analysis and fair adaptation algorithm for multiple competing dynamic adaptive streaming over http clients. In: VCIP 2012, pp 6Google Scholar
  26. 26.
    Müller C, Lederer S, Timmerer C (2012) An evaluation of dynamic adaptive streaming over http in vehicular environments. In: 4Th workshop on mobile video, pp 37–42Google Scholar
  27. 27.
    Pantos R, May W (2012) HTTP Live Streaming.
  28. 28.
    Pinsonand MH, Wolf S (2003) Comparing subjective video quality testing methodologies. In: VCIP, pp 573–582Google Scholar
  29. 29.
    Romero LR (2011) A Dynamic Adaptive HTTP Streaming Video Service for Google Android. Master’s thesis, ICT, KTH, SwedenGoogle Scholar
  30. 30.
    Seufert M, Egger S, Slanina M, Zinner T, Hoßfeld T, Tran-Gia P (2015) A survey on quality of experience of http adaptive streaming. IEEE Commun Surv Tutorials 17(1):469–492CrossRefGoogle Scholar
  31. 31.
    Sieber C, Hoßfeld T, Zinner T, Tran-Gia P, Timmerer C (2013) Implementation and user-centric comparison of a novel adaptation logic for dash with svc. In: IM, pp 1318–1323Google Scholar
  32. 32.
    Spachos P, Li W, Chignell M, Leon-Garcia A, Zucherman L, Jiang J (2015) Acceptability and quality of experience in over the top video. In: IEEE ICC - Workshop on quality of experience-based management for future internet applications and services (qoe-FI)Google Scholar
  33. 33.
    Streijl RC, Winkler S, Hands DS (2016) Mean opinion score (mos) revisited: Methods and applications, limitations and alternatives. Mulimed Syst 22(2):213–227CrossRefGoogle Scholar
  34. 34.
    Thang TC, Pham AT, Nguyen HX, Cuong PL, Kang JW (2012) Video streaming over http with dynamic resource prediction. In: 2012 fourth international conference on Communications and electronics (ICCE). IEEE, pp 130–135Google Scholar
  35. 35.
    Timmerer C, Maiero M, Rainer B (2016) Which adaptation logic? an objective and subjective performance evaluation of http-based adaptive media streaming systems. arXiv:1606.00341
  36. 36.
    Tingyao T, Leekwijck W (2014) Factor selection for reinforcement learning in http adaptive streaming. In: Multimedia modeling. Springer International Publishing, vol 8325, pp 553–567Google Scholar
  37. 37.
    VideoLAN (2008) VLC source code.
  38. 38.
  39. 39.
    Yin X, Sekar V, Sinopoli B (2014) Toward a principled framework to design dynamic adaptive streaming algorithms over http. In: Proceedings of the 13th ACM Workshop on Hot Topics in Networks. ACM, pp 9Google Scholar
  40. 40.
    Zink M, Künzel O, Schmitt J, Steinmetz R (2003) Subjective impression of variations in layer encoded videos. In: IWQOs. Springer, pp 137–154Google Scholar
  41. 41.
    Zink M, Schmitt J, Steinmetz R (2005) Layer-encoded video in scalable adaptive streaming. IEEE Trans Multimed 7(1):75–84CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science, Ariel Cyber Innovation CenterAriel UniversityArielIsrael
  2. 2.Department of Computer ScienceHolon Institute of TechnologyHolonIsrael
  3. 3.Department of Applied MathematicsHolon Institute of TechnologyHolonIsrael
  4. 4.Communication Systems EngineeringBen-Gurion University of the NegevBeershebaIsrael

Personalised recommendations