Skip to main content
SpringerLink
Log in

Audiovisual quality of live music streaming over mobile networks using MPEG-DASH

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

Abstract

The MPEG-DASH protocol has been rapidly adopted by most major network content providers and enables clients to make informed decisions in the context of HTTP streaming, based on network and device conditions using the available media representations. A review of the literature on adaptive streaming over mobile shows that most emphasis has been on adapting the video quality whereas this work examines the trade-off between video and audio quality. In particular, subjective tests were undertaken for live music streaming over emulated mobile networks with MPEG-DASH. A group of audio/video sequences was designed to emulate varying bandwidth arising from network congestion, with varying trade-off between audio and video bit rates. Absolute Category Rating was used to evaluate the relative impact of both audio and video quality in the overall Quality of Experience (QoE). One key finding from the statistical analysis of Mean Opinion Scores (MOS) results using Analysis of Variance indicates that providing reduced audio quality has a much lower impact on QoE than reducing video quality at similar total bandwidth situations. This paper also describes an objective model for audiovisual quality estimation that combines the outcomes from audio and video metrics into a joint parametric model. The correlation between predicted and subjective MOS was computed using several outcomes (Pearson and Spearman correlation coefficients, Root Mean Square Error (RMSE) and epsilon-insensitive RMSE). The obtained results indicate that the proposed approach is a viable solution for objective audiovisual quality assessment in the context of live music streaming over mobile network.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. https://www.ffmpeg.org/

  2. https://bitmovin.com/mpeg-dash-hls-segment-length/

References

  1. Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming - a survey and open challenges. IEEE Access 7:30831–30859

    Article  Google Scholar 

  2. Beerends JG, De Caluwe FE (1999) The influence of video quality on perceived audio quality and vice versa. J Audio Eng Soc 47(5):355–362

    Google Scholar 

  3. Bossen F (2013) Common Test Conditions and Software Reference Configurations (JCTVC-K1100). In: 11th meeting: joint collaborative team on video coding (JCT-VC) of ITU-T SG16

  4. Claeys M, Latre S, Famaey J, De Turck F (2014) Design and Evaluation of a Self-learning HTTP Adaptive Video Streaming Client. IEEE Communications Letters 18(4):716–719

    Article  Google Scholar 

  5. 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, Piscataway, pp 1288–1293

  6. Duanmu Z, Ma K, Wang Z (2018) Quality-of-experience for adaptive streaming videos: an expectation confirmation theory motivated approach. IEEE T Image Process 27(12):6135–6146

    Article  MathSciNet  Google Scholar 

  7. Garcia MN, De Simone F, Tavakoli S, Staelens N, Egger S, Brunnstrom K, Raake A (2014) Quality of experience and HTTP adaptive streaming: a review of subjective studies. In: 6Th international workshop on quality of multimedia experience(QoMEX). IEEE, Piscataway, pp 141–146

  8. Hands DS (2004) A Basic Multimedia Quality Model. IEEE T Multimedia 6 (6):806–816

    Article  Google Scholar 

  9. Herre J, Dietz M (2008) MPEG-4 high-efficiency AAC coding [standards in a nutshell]. IEEE Signal Process Mag 25(3):137–142

    Article  Google Scholar 

  10. Hines A, Gillen E, Kelly D, Skoglund J, Kokaram A, Harte N (2015) ViSQOLAudio: an objective audio quality metric for low bitrate codecs. J Acoust Soc Am 137(6):449–455

    Article  Google Scholar 

  11. Hoßfeld T, Schatz R, Biersack E, Plissonneau L (2013) Internet video delivery in youtube: from traffic measurements to quality of experience. In: Data traffic monitoring and analysis. Springer, Berlin, pp 264–301

  12. Irondi I, Wang Q, Grecos C, Calero JMA, Casaseca-De-La-Higuera P (2019) Efficient QoE-aware scheme for video quality switching operations in dynamic adaptive streaming. ACM T Multim Comput 15(1):1–23

    Article  Google Scholar 

  13. ITU-T Recommendation P.911 (1998) Subjective audiovisual quality assessment methods for multimedia applications. ITU telecommunication Std sector

  14. ITU-R Recommendation BS.1387 (2001) Method for objective measurements of perceived audio quality. ITU radiocommunication Std sector

  15. ITU-T Recommendation P.910 (2008) Subjective video quality assessment methods for multimedia applications. ITU telecommunication Std sector

  16. ITU-T Recommendation J.247 (2008) Objective perceptual multimedia video quality measurement in the presence of a full reference. ITU telecommunication Std sector

  17. ITU-R Recommendation BT.500-13 (2012) Methodology for the subjective assessment of the quality of television pictures. ITU radiocommunication Std sector

  18. ITU-R Recommendation P.1401 (2012) Methods metrics and procedures for statistical evaluation, qualification and comparison of objective quality prediction models. ITU telecommunication Std sector

  19. ITU-T Recommendation P.863 (2014) Perceptual objective listening quality assessment. ITU telecommunication Std sector

  20. ITU-R Recommendation BS.1116-3 (2015) Methods for the subjective assessment of small impairments in audio systems. ITU radiocommunication Std sector

  21. ITU-R Recommendation BS.1534-3 (2015) Method for the subjective assessment of intermediate quality levels of coding systems. ITU radiocommunication Std sector

  22. ITU-T Recommendation P.913 (2016) Methods for the subjective assessment of video quality, audio quality and audiovisual quality of internet video and distribution quality television in any environment. ITU telecommunication Std sector

  23. ITU-T Recommendation P.1203 (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport. ITU telecommunication Std sector

  24. ITU-T Recommendation P.1203.2 (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport – audio quality estimation module. ITU telecommunication Std sector

  25. ITU-T Recommendation P.1203.3 (2017) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport - quality integration module. ITU telecommunication Std sector

  26. ITU-T Recommendation P.1203.1 (2019) Parametric bitstream-based quality assessment of progressive download and adaptive audiovisual streaming services over reliable transport - video quality estimation module. ITU telecommunication Std sector

  27. Liu Y, Dey S, Ulupinar F, Luby M, Mao Y (2015) Deriving and validating user experience model for dash video streaming. IEEE T Broadcast 61(4):651–665

    Article  Google Scholar 

  28. Manasa K, Channappayya SS (2016) An optical flow-based full reference video quality assessment algorithm. IEEE T Image Process 25(6):2480–2492

    Article  MathSciNet  Google Scholar 

  29. Martinez HAB, Farias MC (2018) Combining audio and video metrics to assess audio-visual quality. Multimed Tools Appl 77(18):23993–24012

    Article  Google Scholar 

  30. Massey Jr. FJ (1951) The Kolmogorov-Smirnov test for goodness of fit. J A Stat Assoc 46(253):68–78

    Article  Google Scholar 

  31. Mok RK, Chan EW, Chang RK (2011) Measuring the quality of experience of HTTP video streaming. In: 2011 IFIP/IEEE international symposium on integrated network management. IEEE, Piscataway, pp 485–492

  32. Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE T Broadcast 50(3):312–322

    Article  Google Scholar 

  33. Pinson M, Ingram W, Webster A (2011) Audiovisual quality components. IEEE Signal Process Mag 6(28):60–67

    Article  Google Scholar 

  34. Pocta P, Beerends JG (2015) Subjective and objective assessment of perceived audio quality of current digital audio broadcasting systems and web-casting applications. IEEE T Broadcast 61(3):407–415

    Article  Google Scholar 

  35. Rodrigues R, Pocta P, Melvin H, Pereira M, Pinheiro AM (2016) MPEG DASH – some QoE-based insights into the tradeoff between audio and video for live music concert streaming under congested network conditions. In: 8Th international conference on quality of multimedia experience (QoMEX). IEEE, Piscataway, pp 1–6

  36. Seshadrinathan K, Bovik AC (2009) Motion-based Perceptual Quality Assessment of Video. In: Proceedings of SPIE - Human Vision and Electronic Imaging XIV, vol 7240. International Society for Optics and Photonics, SPIE, Bellingham, pp 283–294

  37. Sodagar I (2011) The MPEG-DASH standard for multimedia streaming over the internet. IEEE Multimedia 18(4):62–67

    Article  Google Scholar 

  38. Soundararajan R, Bovik AC (2013) Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE T Circ Syst Vid 23(4):684–694

    Article  Google Scholar 

  39. Takahashi S, Yamagishi K, Lebreton P, Okamoto J (2019) Impact of quality factors on users’ viewing behaviors in adaptive bitrate streaming services. In: 11Th international conference on quality of multimedia experience(QoMEX). IEEE, Piscataway, pp 1–6

  40. Tavakoli S, Brunnstrøm K, Gutiérrez J, García N (2015) Quality of experience of adaptive video streaming: investigation in service parameters and subjective quality assessment methodology. Signal Process Image Commun 39:432–443

    Article  Google Scholar 

  41. Tavakoli S, Brunnstrøm K, Wang K, Andrén B, Shahid M, Garcia N (2014) Subjective quality assessment of an adaptive video streaming model. In: Proceedings of SPIE - image quality and system performance XI, vol 9016. International Society for Optics and Photonics, SPIE, Bellingham, pp 197–208

  42. Thang TC, Ho QD, Kang JW, Pham AT (2012) Adaptive streaming of audiovisual content using MPEG DASH. IEEE Trans Consum Electron 58(1):78–85

    Article  Google Scholar 

  43. Tran HT, Ngoc NP, Pham AT, Thang TC (2016) A multi-factor QoE model for adaptive streaming over mobile networks. In: 2016 IEEE globecom workshops. IEEE, Piscataway, pp 1–6

  44. Tran HT, Ngoc NP, Hoßfeld T, Thang TC (2018) A cumulative quality model for HTTP adaptive streaming. In: 10Th international conference on quality of multimedia experience(QoMEX). IEEE, Piscataway, pp 1–6

  45. Tran HT, Nguyen DV, Nguyen DD, Ngoc NP, Thang TC (2019) An LSTM-based approach for overall quality prediction in HTTP adaptive streaming. In: IEEE conference on computer communications workshops(INFOCOM WKSHPS). IEEE, Piscataway, pp 702–707

  46. Vu PV, Vu CT, Chandler DM (2011 ) A spatiotemporal most-apparent-distortion model for video quality assessment. In: 18Th IEEE international conference on image processing(ICIP). IEEE, Piscataway, pp 2505–2508

  47. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image Quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  48. Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: 37th IEEE asilomar conference on signals, systems and computers, vol 2. IEEE, Piscataway, pp 1398–1402

  49. Wiegand T, Sullivan GJ, Bjöntegaard G, Luthra A (2003) Overview of the H.264/AVC video coding standard. IEEE T Circ Syst Vid 13(7):560–576

    Article  Google Scholar 

  50. Wilk S, Schönherr S, Stohr D, Effelsberg W (2015) EnvDASH: an environment-aware dynamic adaptive streaming over HTTP system. In: Proceedings of the ACM international conference on interactive experiences for TV and online video. ACM, New York, pp 113–118

  51. Yamagishi K, Hayashi T (2017) Parametric quality-estimation model for adaptive-bitrate-streaming services. IEEE T Multimedia 19(7):1545–1557

    Article  Google Scholar 

  52. You J, Reiter U, Hannuksela MM, Gabbouj M, Perkis A (2010) Perceptual-based quality assessment for audiovisual services: a survey. Signal Process Image Commun 25 (7):482–501

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge QuiVVer - “Centro de competência em Qualidade, Validação e Verificação de software” (CENTRO-07-CT62-FEDER-005009) for providing the equipment used in the subjective tests. Furthermore, we acknowledge the E.U. Action COST IC 1003 - Qualinet for enabling the cooperation between the three research institutions involved in this project.

Author information

Authors and Affiliations

  1. Instituto de Telecomunicações and Universidade da Beira Interior, Covilhã, Portugal

    Rafael Rodrigues, Marco V. Bernardo, Manuela Pereira & Antonio M. G. Pinheiro

  2. Department of Multimedia and Information-Communication Technology, University of Zilina, Zilina, Slovakia

    Peter Pocta

  3. School of Computer Science, National University of Ireland, Galway, Republic of Ireland

    Hugh Melvin

Authors
  1. Rafael Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Pocta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hugh Melvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco V. Bernardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manuela Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Antonio M. G. Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Rodrigues.

Additional information

Publisher’s note

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

This research was co-funded by FEDER-PT2020, Portugal partnership agreement, under the project PTDC/EEI-PRO/2849/2014 -POCI-01-0145-FEDER-016693, Fundação para a Ciência e a Tecnologia (FCT/MCTES) under the project UIDB/EEA/50008/2020 and the Instituto de Telecomunicações -Fundação para a Ciência e a Tecnologia (project UID/EEA/50008/2013) under internal project QoEVIS

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rodrigues, R., Pocta, P., Melvin, H. et al. Audiovisual quality of live music streaming over mobile networks using MPEG-DASH. Multimed Tools Appl 79, 24595–24619 (2020). https://doi.org/10.1007/s11042-020-09047-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09047-6

Keywords

Search

Navigation