An Experimental Study of the Server-based Unfairness Solutions for the Cross-Protocol Scenario of Adaptive Streaming over HTTP/3 and HTTP/2

Main Article Content

Chanh Minh Tran
Tho Nguyen Duc
Phan Xuan Tan
Eiji Kamioka


Since the introduction of the HTTP/3, research has focused on evaluating its influences on the existing adaptive streaming over HTTP (HAS). Among these research, due to irrelevant transport protocols, the cross-protocol unfairness between the HAS over HTTP/3 (HAS/3) and HAS over HTTP/2 (HAS/2) has caught considerable attention. It has been found that the HAS/3 clients tend to request higher bitrates than the HAS/2 clients because the transport QUIC obtains higher bandwidth for its HAS/3 clients than the TCP for its HAS/2 clients. As the problem originates from the transport layer, it is likely that the server-based unfairness solutions can help the clients overcome such a problem. Therefore, in this paper, an experimental study of the server-based unfairness solutions for the cross-protocol scenario of the HAS/3 and HAS/2 is conducted. The results show that, while the bitrate guidance solution fails to help the clients achieve fairness, the bandwidth allocation solution provides superior performance.


Download data is not yet available.

Article Details

How to Cite
Tran CM, Duc TN, Tan PX, Kamioka E. An Experimental Study of the Server-based Unfairness Solutions for the Cross-Protocol Scenario of Adaptive Streaming over HTTP/3 and HTTP/2. Baghdad Sci.J [Internet]. 2021 Dec. 20 [cited 2022 Nov. 30];18(4(Suppl.):1441. Available from:


Hypertext Transfer Protocol Version 3 (HTTP/3) - draft-ietf-quic-http-34 [Internet]. 2021 [cited 2021 Jun 14]. Available from:

QUIC: A UDP-Based Multiplexed and Secure Transport - draft-ietf-quic-transport-34 [Internet]. 2021 [cited 2021 Jun 14]. Available from:

Hassan et al. PWRR Algorithm for Video Streaming Process Using Fog Computing. Baghdad Sci. J. 2019 Sep;16(3):0667.

Seufert M, Schatz R, Wehner N, Gardlo B, Casas P. Is QUIC becoming the New TCP? On the Potential Impact of a New Protocol on Networked Multimedia QoE. In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). Berlin, Germany: IEEE; 2019. p. 1–6.

Mondal A, Chakraborty S. Does QUIC Suit Well With Modern Adaptive Bitrate Streaming Techniques? IEEE Netw. Lett. 2020;2(2):85–9.

Tran CM, Nguyen Duc T, Tan PX, Kamioka E. FAURAS: A Proxy-Based Framework for Ensuring the Fairness of Adaptive Video Streaming over HTTP/2 Server Push. Appl. Sci. 2020;10(7):2485.

Jiang J, Sekar V, Zhang H. Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive. IEEE/ACM Trans. Netw. 2014;22(1):326–40.

Li Z, Zhu X, Gahm J, Pan R, Hu H, Begen AC, et al. Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale. IEEE J. Sel. Areas Commun. 2014;32(4):719–33.

Bhat D, Rizk A, Zink M. Not so QUIC: A Performance Study of DASH over QUIC. In: NOSSDAV’17. San Jose, CA, USA: Association for Computing Machinery; 2017. p. 13–18.

Bhat D, Deshmukh R, Zink M. Improving QoE of ABR Streaming Sessions through QUIC Retransmissions. In: The 26th ACM International Conference on Multimedia. San Jose, CA, USA: ACM; 2018. p. 1616–1624.

Arisu S, Yildiz E, Begen AC. Game of Protocols: Is QUIC Ready for Prime Time Streaming? Int J Netw Manag. 2020;30(3):18.

Tran CM, Nguyen Duc T, Tan PX, Kamioka E. Cross-Protocol Unfairness between Adaptive Streaming Clients over HTTP/3 and HTTP/2: A Root-Cause Analysis. Electronics. 2021;10(15):1755.

QUIC Loss Detection and Congestion Control - draft-ietf-quic-transport-34 [Internet]. 2021 [cited 2021 Jun 14]. Available from:

Wangen G, Shalaginov A, Hallstensen C. Cyber Security Risk Assessment of a DDoS Attack. In: Information Security. Springer International Publishing; 2016.

Altamimi S, Shirmohammadi S. QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning. ACM Trans Multimedia Comput Commun Appl. 2020;16(2s):68.

Guguen CT, Bolzer FL, Houdaille R. Improving User Experience when HTTP Adaptive Streaming Clients Compete for Bandwidth. SMPTE Motion Imaging J. 2017;126(1):28–34.

Spiteri K, Urgaonkar R, Sitaraman RK. BOLA: Near-Optimal Bitrate Adaptation for Online Videos. IEEE/ACM Trans. Netw. 2020;28(4):1698–711.

Jain R, Chiu D-M, Hawe W. A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems. CoRR [Internet]. 1998;cs.NI/9809099. Available from:

Bentaleb A, Taani B, Begen AC, Timmerer C, Zimmermann R. A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP. IEEE Commun. Surveys Tuts. 2019;21(1):562–85.

Dar et al. Fog Computing Resource Optimization: A Review on Current Scenarios and Resource Management. Baghdad Sci. J. 2019 Jun;16(2):0419.