Skip to main content
Log in

MiSAL - A minimal quality representation switch logic for adaptive streaming

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  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 157–168

  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 HAL

  3. Cisco (2017) Cisco visual networking index: Global mobile data traffic forecast update, 2016-2021. Technical report, Cisco

  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):25–43

    Article  Google Scholar 

  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 1–8

  6. Cranley N, Perry P, Murphy L (2006) User perception of adapting video quality. Int J Human-Comput Stud 64(8):637–647

    Article  Google Scholar 

  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 1–8

  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 1288–1293

  9. Dubin R, Hadar O, Dvir A (2013) The effect of client buffer and mbr consideration on dash adaptation logic. In: WCNC, pp 2178–2183

  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):19–31

    Article  Google Scholar 

  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 1–6

  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)

  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 9

  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. 81:320–332

    Article  Google Scholar 

  15. ISO/IEC (2014) Information technology - Dynamic adaptive streaming over HTTP (DASH). Technical report, ISO, USA

  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 environment

  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 97–108

  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 Photonics

  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):1845–1851

    Article  Google Scholar 

  20. Li Y, Du S, Zhou G (2017) Energy optimization for mobile video streaming via an aggregate model. Multimed Tools Appl 76(20):20781–20797

    Article  Google Scholar 

  21. Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. In: ACM Multimedia Systems, CA, pp 169–174

  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 1–8

  23. Mok RKP, Chan EWW, Luo X, Chang RKC (2012) Qdash: a qoe-aware dash system. In: Multimedia systems conference. ACM, pp 11–22

  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):652–671

    Article  Google Scholar 

  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 6

  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 37–42

  27. Pantos R, May W (2012) HTTP Live Streaming. http://tools.ietf.org/html/draft-pantos-http-live-streaming-10

  28. Pinsonand MH, Wolf S (2003) Comparing subjective video quality testing methodologies. In: VCIP, pp 573–582

  29. Romero LR (2011) A Dynamic Adaptive HTTP Streaming Video Service for Google Android. Master’s thesis, ICT, KTH, Sweden

    Google Scholar 

  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):469–492

    Article  Google Scholar 

  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 1318–1323

  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)

  33. Streijl RC, Winkler S, Hands DS (2016) Mean opinion score (mos) revisited: Methods and applications, limitations and alternatives. Mulimed Syst 22(2):213–227

    Article  Google Scholar 

  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 130–135

  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. Tingyao T, Leekwijck W (2014) Factor selection for reinforcement learning in http adaptive streaming. In: Multimedia modeling. Springer International Publishing, vol 8325, pp 553–567

  37. VideoLAN (2008) VLC source code. http://www.videolan.org/vlc/download-sources.html

  38. Wireshark. http://www.wireshark.org/

  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 9

  40. Zink M, Künzel O, Schmitt J, Steinmetz R (2003) Subjective impression of variations in layer encoded videos. In: IWQOs. Springer, pp 137–154

  41. Zink M, Schmitt J, Steinmetz R (2005) Layer-encoded video in scalable adaptive streaming. IEEE Trans Multimed 7(1):75–84

    Article  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Amit Dvir.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dvir, A., Harel, N., Dubin, R. et al. MiSAL - A minimal quality representation switch logic for adaptive streaming. Multimed Tools Appl 78, 26483–26508 (2019). https://doi.org/10.1007/s11042-019-07865-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-07865-x

Keywords

Navigation