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.
Similar content being viewed by others
Notes
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].
The average omitted video chunk size of corresponding adaptation algorithm for all throughput waveforms.
sDASH term will be used to refer this implementation in the rest of the paper.
References
Cisco, Cisco Visual Networking Index (VNI) Complete Forecast Update, 2017–2022 (2018)
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)
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)
Zabrovskiy, A., Petrov, E., Kuzmin, E., and Timmerer, C.: Evaluation of the performance of adaptive HTTP streaming systems. Preprint at arXiv:1710.02459 (2017)
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)
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)
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)
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)
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)
Lederer, S., Müller, C., Timmerer, C.: Dynamic adaptive streaming over HTTP dataset. In: Proceedings of the 3rd Multimedia Systems Conference, pp. 89–94 (2012)
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)
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)
Zabrovskiy, A., Feldmann, C., Timmerer, C.: Multi-codec DASH dataset. In: Proceedings of the 9th ACM Multimedia Systems Conference, pp. 438–443 (2018)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Link to be supplied
Çalı, M., and Ozbek, N.: Dash-Simulator. GitHub repository, https://github.com/mehmet-cali/dash-simulator (2021)
Reichel, J., Schwarz, H., Wien, M.: Joint scalable video model 11 (JSVM 11). Joint Video Team, pp. 23 (2007)
Blender Foundation: Big Buck Bunny. https://peach.blender.org/. Accessed 29 April 2020
Blender Foundation: Sintel, the Durian Open Movie Project. https://durian.blender.org/. Accessed 29 April 2020
Blender Foundation: Tears of Steel. https://mango.blender.org/. Accessed 29 April 2020
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)
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)
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)
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)
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)
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)
Funding
The online version supplementary material available at https://doi.org/10.1007/s11760-021-01880-y.
Author information
Authors and Affiliations
Corresponding author
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.
Rights and permissions
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-021-01880-y