## 2 Related Work  
The behavior of multiple clients sharing a fixed bottleneck bandwidth in adaptive streaming over HTTP/1.1 was first investigated in [18]. A performance evaluation of major commercial and open source adaptive streaming players with two competing clients was conducted, showing that the unfairness in bitrate selection occurred. The authors argued that the issue was not related to the well-known congestion control mechanism of TCP but to the mismatch of bandwidth estimations among the clients. This finding was confirmed and investigated further in [19]. It was discovered that, due to the temporal overlap of the clients' segment download states, which were called the _ON-OFF periods_, a client might overestimate its available bandwidth, thus selecting a too high bitrate version and unfairly occupying the utilization for other clients. As a result, several attempts have been carried out to solve the unfairness in adaptive streaming over HTTP/1.1.  
Existing solutions to the unfairness of HAS in HTTP/1.1 can be categorized into client-based, server-based and network-assisted methods [11]. Client-based adaptation schemes [21; 22; 23; 24; 25] make use of the computational power of clients' devices to adapt the video bitrate based on measurements of different adaptation metrics, such as available bandwidth, playback buffer, instantaneous QoE, etc. Although benefiting the system deployment and scalability, such methods usually perform suboptimally due to insufficient information regarding the entire network conditions [35]. On the other hand, server-based solutions [26; 27; 28; 29] utilize the advantages of centralized video servers to perform overall bitrate optimization and exchange insights about the clients' statuses. However, these solutions either limit the system scalability when the number of clients increases or introduce high overhead and complexity that may harm the network efficiency [10; 11]. Meanwhile, network-assisted approaches [30; 31; 32; 33; 34] employ in-network entities (e.g., network edges, controllers, proxies, etc.) to assist the clients' bitrate decisions. Thanks to the general observation of the network, such approaches have shown a high performance in both small and large networks. A bandwidth shaping mechanism was deployed at the residential gateway in [30; 31] to assign fair bandwidth slices to the clients. As such, the bandwidth competition was avoided and the bitrate difference among the clients were minimized. In [32], the authors discussed the use of a network proxy in overwriting the client's bitrate request to meet with the calculated fair share. The work in [33] compared the performance of different Software-Defined Network based (SDN-based) methods, including bandwidth shaping, bitrate guidance, and hybrid method, found out that all methods provided remarkable improvement of fairness. Likewise, several proxy-based methods were evaluated in [34] considering the fairness in terms of user's QoE under the Long-Term Evolution (LTE) network. The aforementioned research are crucial for the mass adoption of the adaptive streaming technology. However, to the best of our knowledge, no existing work has ever considered the unfairness problem when utilizing the HTTP/2 server push.  
The existence of the unfairness in adaptive streaming over HTTP/2 server push has been confirmed in our previous work [20]. Therefore, in this paper, we set light for solving such a problem with a network-assisted approach by proposing the FAURAS framework.