Skip to main content
SpringerLink
Log in

Time-efficient evaluation of adaptation algorithms for DASH with SVC: dataset, throughput generation and stream simulator

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Bitrate adaptation algorithms have received considerable attention recently. In order to evaluate these algorithms objectively, multiple DASH datasets have been proposed. However, only few of them are compatible to SVC-based adaptation algorithms. Apart from the dataset, to fully implement and evaluate an adaptation algorithm, many time-consuming steps are required such as MPD parser design, adaptation logic design and network environment setup. In this paper, a dash simulator which assesses the performance of SVC-based adaptation algorithms without the requirement of any additional implementation steps is proposed. Also, an SVC dataset that includes both CBR and VBR encoded videos is designed. Demonstration is performed as evaluation of an SVC-based adaptation algorithm under several throughput scenarios using the designed dataset. Results show that the proposed system considerably reduces time requirement compared to real-time assessment. Dataset, throughput generation tool and simulator are all publicly available so that the researchers can test their implementation and compare with the results presented in this paper.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. In the rest of the paper, we describe the process with a window length of 30 samples which is the same value used in [34].

  2. The average omitted video chunk size of corresponding adaptation algorithm for all throughput waveforms.

  3. sDASH term will be used to refer this implementation in the rest of the paper.

References

  1. Cisco, Cisco Visual Networking Index (VNI) Complete Forecast Update, 2017–2022 (2018)

  2. Stockhammer, T.: Dynamic adaptive streaming over HTTP: standards and design principles. In: Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp. 133–144 (2016)

  3. Ayad, I., Im, Y., Keller, E., Ha, S.: A practical evaluation of rate adaptation algorithms in http-based adaptive streaming. Comput. Netw. 133, 90–103 (2018)

    Article  Google Scholar 

  4. Zabrovskiy, A., Petrov, E., Kuzmin, E., and Timmerer, C.: Evaluation of the performance of adaptive HTTP streaming systems. Preprint at arXiv:1710.02459 (2017)

  5. Vergados, D.J., Kralevska, K., Michalas, A., Vergados, D.D.: Evaluation of HTTP/DASH adaptation algorithms on vehicular networks. In: 2018 Global Information Infrastructure and Networking Symposium (GIIS), pp. 1–5. IEEE (2018)

  6. Vergados, D.J., Michalas, A., Sgora, A., Vergados, D.D., Chatzimisios, P.: FDASH: a fuzzy-based MPEG/DASH adaptation algorithm. IEEE Syst. J. 10(2), 859–868 (2015)

    Article  Google Scholar 

  7. Schwarz, H., Marpe, D., Wiegand, T.: Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Trans. Circuits Syst. Video Technol. 17(9), 1103–1120 (2007)

    Article  Google Scholar 

  8. Zhao, M., Gong, X., Liang, J., Wang, W., Que, X., Cheng, S.: QoE-driven cross-layer optimization for wireless dynamic adaptive streaming of scalable videos over HTTP. IEEE Trans. Circuits Syst. Video Technol. 25(3), 451–465 (2015)

    Article  Google Scholar 

  9. Kalva, H., Adzic, V., Furht, B.: Comparing MPEG AVC and SVC for adaptive HTTP streaming. In: 2012 IEEE International Conference on Consumer Electronics (ICCE), pp. 158–159. IEEE (2012)

  10. Lederer, S., Müller, C., Timmerer, C.: Dynamic adaptive streaming over HTTP dataset. In: Proceedings of the 3rd Multimedia Systems Conference, pp. 89–94 (2012)

  11. Le Feuvre, J., Thiesse, J. M., Parmentier, M., Raulet, M., Daguet, C.: Ultra high definition HEVC DASH data set. In: Proceedings of the 5th ACM Multimedia Systems Conference, pp. 7–12 (2014)

  12. Quinlan, J.J., Zahran, A.H., Sreenan, C.J.: Datasets for AVC (H. 264) and HEVC (H. 265) evaluation of dynamic adaptive streaming over HTTP (DASH). In: Proceedings of the 7th International Conference on Multimedia System, pp. 1–6 (2016)

  13. Zabrovskiy, A., Feldmann, C., Timmerer, C.: Multi-codec DASH dataset. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 438–443 (2018)

  14. Kreuzberger, C., Posch, D., Hellwagner, H.: A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP. In: Proceedings of the 6th ACM Multimedia Systems Conference, pp. 213–218. ACM (2015)

  15. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  16. Xiong, P., Shen, J., Wang, Q., Jayasinghe, D., Li, J., Pu, C.: NBS: a network-bandwidth-aware streaming version switcher for mobile streaming applications under fuzzy logic control. In: 2012 IEEE First International Conference on Mobile Services, pp. 48–55. IEEE (2012)

  17. Zhou, C., Lin, C.-W., Guo, Z.: mDASH: a Markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. Multimedia 18(4), 738–751 (2016)

    Article  Google Scholar 

  18. Mori, S., Bandai, M.: QoE-aware quality selection method for adaptive video streaming with scalable video coding. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–4. IEEE (2018)

  19. Sieber, C., Hoßfeld, T., Zinner, T., Tran-Gia, P., Timmerer, C.: Implementation and user-centric comparison of a novel adaptation logic for DASH with SVC. In: 2013 IFIP/IEEE International Symposium on Integerated Network Management (IM 2013), pp. 1318–1323. IEEE (2013)

  20. Spiteri, K., Urgaonkar, R., Sitaraman, R.K.: BOLA: near-optimal bitrate adaptation for online videos. In IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)

  21. Van der Hooft, J., Petrangeli, S., Bouten, N., Wauters, T., Huysegems, R., Bostoen, T., De Turck, F.: An HTTP/2 push-based approach for SVC adaptive streaming. In: NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, pp. 104–111. IEEE (2016)

  22. Riiser, H., Endestad, T., Vigmostad, P., Griwodz, C., Halvorsen, P.: Video streaming using a location-based bandwidth-lookup service for bitrate planning. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 8(3), 1–19 (2012)

    Article  Google Scholar 

  23. 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, pp. 114–118 (2013)

  24. Raca, D., Quinlan, J.J., Zahran, A.H., Sreenan, C.J.: Beyond throughput: a 4G LTE dataset with channel and context metrics. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 460–465 (2018)

  25. Timmerer, C., Zabrovskiy, A., Begen, A.: Automated objective and subjective evaluation of HTTP adaptive streaming systems. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 362–367. IEEE (2018)

  26. Link to be supplied

  27. Çalı, M., and Ozbek, N.: Dash-Simulator. GitHub repository, https://github.com/mehmet-cali/dash-simulator (2021)

  28. Reichel, J., Schwarz, H., Wien, M.: Joint scalable video model 11 (JSVM 11). Joint Video Team, pp. 23 (2007)

  29. Blender Foundation: Big Buck Bunny. https://peach.blender.org/. Accessed 29 April 2020

  30. Blender Foundation: Sintel, the Durian Open Movie Project. https://durian.blender.org/. Accessed 29 April 2020

  31. Blender Foundation: Tears of Steel. https://mango.blender.org/. Accessed 29 April 2020

  32. Flynn, J. R., Ward, S., Abich, J., Poole, D.: Image quality assessment using the ssim and the just noticeable difference paradigm. In: International Conference on Engineering Psychology and Cognitive Ergonomics, pp. 23–30. Springer, Berlin (2013)

  33. Mirza, M., Sommers, J., Barford, P., Zhu, X.: A machine learning approach to TCP throughput prediction. ACM SIGMETRICS Perform. Eval. Rev. 35(1), 97–108 (2007)

    Article  Google Scholar 

  34. Yoshida, H., Satoda, K., Murase, T.: Constructing stochastic model of TCP throughput on basis of stationarity analysis. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1544–1550. IEEE (2013)

  35. Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366a), 427–431 (1979)

    Article  MathSciNet  Google Scholar 

  36. Netravali, R., Sivaraman, A., Das, S., Goyal, A., Winstein, K., Mickens, J., Balakrishnan, H.: Mahimahi: accurate record-and-replay for HTTP. In: 2015 USENIX Annual Technical Conference (USENIXATC 15), pp. 417–429 (2015)

  37. Lekharu, A., Kumar, S., Sur, A., Sarkar, A.: A QoE aware SVC based client-side video adaptation algorithm for cellular networks. In: Proceedings of the 19th International Conference on Distributed Computing and Networking, pp. 1–4 (2018)

Download references

Funding

The online version supplementary material available at https://doi.org/10.1007/s11760-021-01880-y.

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Izmir Institute of Technology, Izmir, Turkey

    Mehmet Çalı

  2. Department of Electrical and Electronics Engineering, Ege University, Izmir, Turkey

    Nükhet Özbek

Authors
  1. Mehmet Çalı
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nükhet Özbek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nükhet Özbek.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (docx 178 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çalı, M., Özbek, N. Time-efficient evaluation of adaptation algorithms for DASH with SVC: dataset, throughput generation and stream simulator. SIViP 15, 1477–1485 (2021). https://doi.org/10.1007/s11760-021-01880-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01880-y

Keywords

Search

Navigation