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International Conference on Multimedia Modeling

MMM 2022: MultiMedia Modeling pp 394–406Cite as

ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13142)

Abstract

As the video streaming traffic in mobile networks is increasing, improving the content delivery process becomes crucial, e.g., by utilizing edge computing support. At an edge node, we can deploy adaptive bitrate (ABR) algorithms with a better understanding of network behavior and access to radio and player metrics. In this work, we present ECAS-ML, Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming with Machine Learning. ECAS-ML focuses on managing the tradeoff among bitrate, segment switches and stalls to achieve a higher quality of experience (QoE). For that purpose, we use machine learning techniques to analyze radio throughput traces and predict the best parameters of our algorithm to achieve better performance. The results show that ECAS-ML outperforms other client-based and edge-based ABR algorithms.

Keywords

  • HTTP Adaptive Streaming
  • Edge computing
  • Content delivery
  • Network-assisted video streaming
  • Quality of experience
  • Machine learning

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Notes

  1. 1.

    http://ftp.itec.aau.at/datasets/DASHDataset2014/BigBuckBunny/2sec/.

References

  1. 3GPP: 3GPP TS 26.247. Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH). Technical report (2015)

    Google Scholar 

  2. Aguilar-Armijo, J., Timmerer, C., Hellwagner, H.: EADAS: edge assisted adaptation scheme for HTTP adaptive streaming. In: Proceedings of 46th Conference on IEEE Local Computer Networks (LCN) (2021)

    Google Scholar 

  3. Belmoukadam, O., Jawad Khokhar, M., Barakat, C.: On accounting for screen resolution in adaptive video streaming: QoE-driven bandwidth sharing framework. Int. J. Netw. Manage 31(1), e2128 (2021)

    CrossRef  Google Scholar 

  4. Bentaleb, A., Taani, B., Begen, A.C., Timmerer, C., Zimmermann, R.: A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surv. Tutor. 21(1), 562–585 (2018)

    CrossRef  Google Scholar 

  5. Bhat, D., Rizk, A., Zink, M., Steinmetz, R.: SABR: network-assisted content distribution for QoE-driven ABR video streaming. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 14(2s), 1–25 (2018)

    Google Scholar 

  6. Chang, Z., Zhou, X., Wang, Z., Li, H., Zhang, X.: Edge-assisted adaptive video streaming with deep learning in mobile edge networks. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE (2019)

    Google Scholar 

  7. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014)

    Google Scholar 

  8. ETSI: Mobile Edge Computing (MEC); Radio Network Information API. Technical report. Accessed: October 2020

    Google Scholar 

  9. Fajardo, J.O., Taboada, I., Liberal, F.: Improving content delivery efficiency through multi-layer mobile edge adaptation. IEEE Netw. 29(6), 40–46 (2015)

    CrossRef  Google Scholar 

  10. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    CrossRef  Google Scholar 

  11. Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, V.: Mobile edge computing-a key technology towards 5G. ETSI White Paper 11(11), 1–16 (2015)

    Google Scholar 

  12. Huang, T.Y., Johari, R., McKeown, N., Trunnell, M., Watson, M.: A buffer-based approach to rate adaptation: evidence from a large video streaming service. ACM SIGCOMM Comput. Commun. Rev. 44(4), 187–198 (2014)

    CrossRef  Google Scholar 

  13. Huber, P.J.: Robust estimation of a location parameter. In: Breakthroughs in Statistics, pp. 492–518. Springer, Cham (1992). https://doi.org/10.1007/978-1-4612-4380-9_35

  14. Juluri, P., Tamarapalli, V., Medhi, D.: SARA: segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. In: Proceedings of IEEE International Conference on Communication Workshops (ICCW), pp. 1765–1770 (2015)

    Google Scholar 

  15. Kim, M., Chung, K.: Edge computing assisted adaptive streaming scheme for mobile networks. IEEE Access 9, 2142–2152 (2021)

    CrossRef  Google Scholar 

  16. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

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

    Google Scholar 

  18. Ma, X., Li, Q., Chai, J., Xiao, X., Xia, S.T., Jiang, Y.: Steward: smart edge based joint QoE optimization for adaptive video streaming. In: Proceedings of 29th ACM NOSSDAV Workshop, pp. 31–36 (2019)

    Google Scholar 

  19. Nguyen, D.V., Le, H.T., Nam, P.N., Pham, A.T., Thang, T.C.: Adaptation method for video streaming over HTTP/2. IEICE Commun. Express 5(3), 69–73 (2016)

    CrossRef  Google Scholar 

  20. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International Conference on Machine Learning, pp. 1310–1318. PMLR (2013)

    Google Scholar 

  21. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8024–8035 (2019)

    Google Scholar 

  22. 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 9th ACM Multimedia Systems Conference, pp. 460–465 (2018)

    Google Scholar 

  23. Robitza, W., et al.: HTTP adaptive streaming QoE estimation with ITU-T Rec. P.1203 - open databases and software. In: Proceedings of 9th ACM Multimedia Systems Conference, Amsterdam (2018). https://doi.org/10.1145/3204949.3208124

  24. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)

    CrossRef  Google Scholar 

  25. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  26. Yan, Z., Xue, J., Chen, C.W.: Prius: hybrid edge cloud and client adaptation for HTTP adaptive streaming in cellular networks. IEEE Trans. Circuits Syst. Video Technol. 27(1), 209–222 (2016)

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Christian Doppler Laboratory ATHENA, Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Jesús Aguilar-Armijo, Ekrem Çetinkaya, Christian Timmerer & Hermann Hellwagner

Authors
  1. Jesús Aguilar-Armijo
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  2. Ekrem Çetinkaya
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  3. Christian Timmerer
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  4. Hermann Hellwagner
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Corresponding author

Correspondence to Jesús Aguilar-Armijo .

Editor information

Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Björn Þór Jónsson

  2. Dublin City University, Dublin, Ireland

    Dr. Cathal Gurrin

  3. University of Science, VNU-HCM, Ho Chi Minh City, Vietnam

    Minh-Triet Tran

  4. University of Bergen, Bergen, Norway

    Duc-Tien Dang-Nguyen

  5. National Tsing Hua University, Hsinchu, Taiwan

    Anita Min-Chun Hu

  6. Hanoi University of Science and Technology, Hanoi, Vietnam

    Binh Huynh Thi Thanh

  7. Median Technologies, Valbonne, France

    Dr. Benoit Huet

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Aguilar-Armijo, J., Çetinkaya, E., Timmerer, C., Hellwagner, H. (2022). ECAS-ML: Edge Computing Assisted Adaptation Scheme with Machine Learning for HTTP Adaptive Streaming. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_33

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  • DOI: https://doi.org/10.1007/978-3-030-98355-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98354-3

  • Online ISBN: 978-3-030-98355-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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