Abstract
With the COVID19 pandemic, video streaming traffic is increasing rapidly. Especially, the live streaming traffic accounts for large amount due to the fact that many events have been switched to the online forms. Therefore, the demand to ensure a high-quality streaming experience is increasing urgently. Since the network condition is expected to fluctuate dynamically, the video streaming needs to be controlled adaptively according to the network condition to provide high quality of experience (QoE) for users. In this paper, a method was proposed to control the live video streaming using the actor-critic reinforcement learning (RL) technique. In this method, the historical video streaming logs such as throughput, buffer size, rebuffering time, latency are taken consideration as the states of RL, then the model is established to map the states to an action such as bitrate decision. In this study, the live streaming simulation is utilized to evaluate the method since the model needs training and the simulation can generate data much faster than real experiment. Experiments were conducted to evaluate the proposed method. Results demonstrate that the total QoE in Bus and Car scenarios show the best performance. The QoE of Tram case shows the lowest due to the low bandwidth.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sani, Y., Mauthe, A., Edwards, C.: Adaptive bitrate selection: a survey. IEEE Commun. Surv. Tutor. 19(4), 2985–3014 (2017)
Miller, K., Al-Tamimi, A.K., Wolisz, A.: QoE-based low-delay live streaming using throughput predictions. ACM Trans. Multimed. Comput. Commun. Appl. 13(1), 4–41 (2016)
Sodagar, I.: The MPEG-DASH standard for multimedia streaming over the internet. IEEE Multimedia 18(4), 62–67 (2011)
Kua, J., Armitage, G., Branch, P.: A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun. Surv. Tutor. 19(3), 1842–1866 (2017)
Bouzakaria, M., Concolato, C., Feuvre, J.L.: Overhead and performance of low latency live streaming using MPEG-DASH. In: Proceedings of IISA 2014, pp. 92–97. United States (2014)
Wang, B., Ren, F., Zhou, C.: Hybrid control-based ABR: towards low-delay live streaming. In: Proceedings of ICME 2019, pp. 754–759. Shanghai, China (2019)
Wei, B., Song, H., Wang, S., Kanai, K., Katto, J.: Evaluation of throughput prediction for adaptive bitrate control using trace-based emulation. IEEE Access 7, 51346–51356 (2019)
Wei, B., Okano, M., Kanai, K., Kawakami, W., Katto, J.: Throughput prediction using recurrent neural network model. In: Proceedings IEEE 7th Global Conference on Consumer Electronics (GCCE), pp. 107–108. Nara, Japan (2018)
He, Q., Dovrolis, C., Ammar, M.: On the predictability of large transfer TCP throughput. ACM SIGCOMM Comp. Commun. Rev. 35(4), 145–156 (2005)
Liu, Y., Lee, J.Y.: An empirical study of throughput prediction in mobile data networks. In: Proceedings of IEEE GLOBECOM 2015, pp. 1–6. San Diego, CA, USA (2015)
Wei, B., Kanai, K., Kawakami, W., Katto, J.: HOAH: a hybrid TCP throughput prediction with autoregressive model and hidden markov model for mobile networks. In: IEICE Transactions on Communications, E101. B(7), pp. 1612–1624 (2018)
Wei, B., Kawakami, W., Kanai, K., Katto, J., Wang, S.: TRUST: a TCP throughput prediction method in mobile networks. In: Proceedings of IEEE Global Commun. Conference (GLOBECOM), pp. 1–6. Abu Dhabi, UAE (2018)
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. In: Proceedings of ACM SIGCOMM 2014, pp. 187–198. Chicago, IL, USA (2014)
Spiteri, K., Urgaonkar, R., Sitaraman, R.K.: BOLA: near-optimal bitrate adaptation for online videos. In: Proceedings of IEEE INFOCOM 2016, pp. 1–9. San Francisco, CA, USA (2016)
Yin, X., Jindal, A., Sekar, V., Sinopoli, B.: A control-theoretic approach for dynamic adaptive video streaming over HTTP. ACM SIGCOMM Comp. Commun. Rev. 45(4), 325–338 (2015)
Mao, H., Netravali, R., Alizadeh, M.: Neural adaptive video streaming with pensieve. In: Proceedings of ACM SIGCOMM 2017, pp. 197–210. Los Angeles, CA, USA (2017)
Wei, B., Song, H., Wang, S., Katto, J.: Performance analysis of adaptive bitrate algorithms for multi-user DASH video streaming. In: Proceedings of IEEE WCNC 2021, pp. 1–6. Nanjing, China (2021)
Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive. IEEE/ACM Trans. Netw. 22(1), 326–340 (2014)
Li, Z., et al.: Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE J. Sel. Areas Commun. 32(4), 719–733 (2014)
Zhou, C., Lin, C.W., Zhang, X., Guo, Z.: TFDASH: a fairness, stability, and efficiency aware rate control approach for multiple clients over DASH. IEEE Trans. Circuits Syst. Video Technol. 29(1), 198–211 (2019)
Wei, B., Song, H., Katto, J.: FRAB: a flexible relaxation method for fair, stable, efficient multi-user dash video streaming. In: Proceedings of IEEE ICC 2021, pp.1–6. Montreal, Canada (2021)
HSDPA Dataset. http://home.ifi.uio.no/paalh/dataset/hsdpa-tcp-logs
Acknowledgement
This research is supported by JSPS KAKENHI Grant Number 20K14740 and Waseda University Grant for Special Research Projects (Project Number: 2021C-132, 2021E-013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wei, B., Song, H., Nguyen, Q.N., Katto, J. (2022). DASH Live Video Streaming Control Using Actor-Critic Reinforcement Learning Method. In: Calafate, C.T., Chen, X., Wu, Y. (eds) Mobile Networks and Management. MONAMI 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-94763-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-94763-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94762-0
Online ISBN: 978-3-030-94763-7
eBook Packages: Computer ScienceComputer Science (R0)