Abstract
Nowadays, the QoE unfairness problem exists under the scenario of multiple clients sharing bottleneck links, as clients just maximize their Quality of Experience (QoE) independently via adaptive bitrate algorithm and congestion control algorithms provide only connection-level fairness. Improving the QoE fairness among clients, the video providers would jointly optimize the QoE of multiple clients. Nevertheless, existing solutions are impractical due to either deployment issues or heavy computation overhead. Therefore, we propose the practical bandwidth allocation (PBA) mechanism for video QoE fairness in this paper. Specifically, PBA formulates QoE by considering the impacts of both network conditions and devices and reconfigures congestion control algorithm according to the piggybacked QoE. In this distributive manner, PBA can rapidly converge to the bandwidth allocation with good QoE fairness. Real-world experiments confirm that PBA improves the QoE fairness and accordingly increases the minimum QoE of video streams by at least 13% and 15% compared with Copa and Cubic, respectively. Moreover, the performance advantage of PBA becomes significant under dynamic wireless networks.
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Cisco, Visual Networking Index: Forecast and Methodology 2016–2021, September 2017
Akhtar, Z., Nam, Y.S., et al.: Oboe: auto-tuning video ABR algorithms to network conditions. In: Proceedings of SIGCOMM, pp. 44–58, August 2018
Akamai. dash.js. https://github.com/bbc/dash.js
Mholt. Caddy. https://github.com/caddyserver/caddy
Ha, S., Rhee, I., Xu, L.: CUBIC: a new TCP-friendly high-speed TCP variant. ACM SIGOPS Oper. Syst. Rev. 42(5), 64–74 (2008)
Nathan, V., Sivaraman, V., et al.: End-to-end transport for video QoE fairness. In: Proceedings of ACM SIGCOMM, pp. 408–423 (2019)
Mok, R.P.K., Chan, E.W.W., et al.: Inferring the QoE of HTTP video streaming from user-viewing activities. In: Proceedings of the First ACM SIGCOMM Workshop on Measurements up the Stack, pp. 31–36, August 2011
Cardwell, N., Cheng, Y., et al.: BBR: congestion-based congestion control. Queue 14(5), 20–53 (2016)
Arun, V., Balakrishnan, H.: Copa: practical delay-based congestion control for the internet. In: NSDI, pp. 329–342 (2018)
Dong, M., Zarchy, D., et al.: PCC vivace: online-learning congestion control. In: NSDI, pp. 343–356 (2018)
Yin, X., Bartulovié, M., et al.: On the efficiency and fairness of multiplayer HTTP-based adaptive video streaming. In: 2017 American Control Conference (ACC), pp. 4236–4241, May 2017
Li, Z., Katsavounidis, I., et al.: Toward a practical perceptual video quality metric. The Netflix Tech Blog 6, 2 (2016)
Salomon, D.: Data Compression: The Complete Reference. Springer, Heidelberg (2004)
Yin, X., Jindal, A., et al.: A control-theoretic approach for dynamic adaptive video streaming over HTTP. In: Proceedings of ACM SIGCOMM, pp. 325–338, August 2015
Lederer, S., Mueller, C., et al.: Distributed DASH dataset. In: Proceedings of the 4th ACM Multimedia Systems Conference, pp. 131–135, February 2013
Netravali, R., Sivaraman, A., et al.: Mahimahi: accurate record-and-replay for HTTP. In: USENIX ATC, pp. 417–429 (2015)
Triyason, T., Krathu, W.: The impact of screen size toward QoE of cloud-based virtual desktop. Comput. Sci. 111, 203–208 (2017)
Spiteri, K., Urgaonkar, R., et al.: BOLA: near-optimal bitrate adaptation for online videos. IEEE/ACM Trans. Networking 28(4), 1698–1711 (2020)
Abbasloo, S., Yen, C.Y., Chao, H.J.: Classic Meets Modern: a Pragmatic Learning-Based Congestion Control for the Internet, pp. 632–647. SIGCOMM, New York (2020)
Mansy, A., Fayed, M., Ammar, M.: Network-layer fairness for adaptive video streams. In: IFIP Networking, Toulouse, France, pp. 1–9 (2015)
Brunnström, K., et al.: Qualinet white paper on definitions of quality of experience. In: European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003), March 2013
Acknowledgments
This work is partly supported by CERNET Innovation Project under Grant No. NGII20190112 and National Science Foundation of China under Grant No. 61972421, Innovation-Driven Project of Central South University under Grant No. 2020CX033.
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Jiang, W., Ning, P., Zhang, Z., Hu, J., Ren, Z., Wang, J. (2021). Practical Bandwidth Allocation for Video QoE Fairness. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_41
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DOI: https://doi.org/10.1007/978-3-030-85928-2_41
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