Abstract
With the introduction of HTTP/3, whose transport is no longer the traditional TCP protocol but the novel QUIC protocol, research for solutions to the unfairness of Adaptive Streaming over HTTP (HAS) has become more challenging. In other words, because of different transport layers, the HTTP/3 may not be available for some networks and the clients have to use HTTP/2 for their HAS applications instead. Therefore, the scenario in which HAS over HTTP/3 (HAS/3) competes against HTTP/2 (HAS/2) must be considered seriously. However, there has been a shortage of investigations on the performance and the origin of the unfairness in such a cross-protocol scenario in order to produce proper solutions. Therefore, this paper provides a performance evaluation and root-cause analysis of the cross-protocol unfairness between HAS/3 and HAS/2. It is concluded that, due to differences in the congestion control mechanisms of QUIC and TCP, HAS/3 clients obtain larger congestion windows, thus requesting higher video bitrates than HAS/2. As the problem lies in the transport layer, existing client-side ABR-based solutions for the unfairness from the application layer may perform suboptimally for the cross-protocol case.
Highlights
Adaptive streaming over HTTP (HAS) has become the de facto standard for most of the online video services on the internet nowadays, thanks to its ability to instantaneously adapt the video quality with the network condition [1]
This paper presents an up-to-date performance analysis of the HAS/3 and HAS/2 clients under a cross-protocol scenario and confirms that HAS/3 clients always unfairly acquire higher bitrates than its successors
Looking into the transport layer, it is found that the root cause of this underperformance lies in the aggressive occupation of the congestion window of QUIC—the transport of HAS/3
Summary
Adaptive streaming over HTTP (HAS) has become the de facto standard for most of the online video services on the internet nowadays, thanks to its ability to instantaneously adapt the video quality with the network condition [1]. Every streaming client is equipped with an Adaptive Bitrate Algorithm (ABR) in its video player to continuously monitor the network condition, requesting the suitable bitrate for every video segment Undesirable incidents such as playback stalls and quality variation can be reduced, maintaining a high quality of experience (QoE) for the users. It has been proven that due to the mismatch of the clients’ downloading states originating from their ABRs on the application layer, some clients may overestimate their bandwidths and request higher video bitrates than others [4,5,6] Such a phenomenon is defined as the unfairness problem, which causes QoE deterioration and a negative impact on the user retention rate. Over the years, various research has been conducted and various solutions to the unfairness have been proposed [7,8,9,10]
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