## 1 Introduction  
In recent years, video traffic in the network has shown a rapid growth trend, mainly from HTTP streaming media and video-on-demand services, which aim to provide users with the highest quality of experience (QoE). Therefore, many previous studies have focused on designing a more powerful adaptive bitrate algorithm (ABR) to improve the QoE of a single video user. With the growth of video traffic and network connection equipment, the possibility of multiple video streams competing for bandwidth in the same bottleneck link in the network is becoming higher and higher [1]. Therefore, QoE fairness among multiple video users has become a crucial issue. At present, the fairness scheduling scheme for commercial video services across multiple users still focuses on allocating the same bandwidth for each user as much as possible. That is, each video stream independently makes adaptive bitrate decisions through its own ABR algorithm and then divides the bandwidth for each video stream in the bottleneck link as equally as possible by the congestion control protocol. Unfortunately, although these schemes can allocate the same bandwidth to each user, QoE fairness among video users cannot be ensured due to the viewing conditions of different users and the differences in on-demand video content. To study the impact of differences in video content on perceptual quality, we randomly sampled videos from two different genres (science fiction and animation) and measured their average perceptual video quality at several different bitrates. The results are shown in Figure 1. Under the same bandwidth conditions, users of video 2 obtain higher viewing quality than users of video 1. The researches attempted to improve QoE fairness among multiple video streams by actively allocating bandwidth to each video stream in recent years. Although this method has achieved good results, it still faces the following problems:  
1. Because of the complexity and time variability of the network, it is difficult to determine when congestion occurs among streams. Bandwidth allocation may lead to a decline in link utilization;
2. Because of the instability of multiple video streams, the behavior of each video stream affects the playback state of other video streams, which may lead to the algorithm being unable to learn the optimal strategy or even losing the guarantee of convergence. A recent trend is to solve related problems in the field of video streaming through deep reinforcement learning (DRL). The video system itself can provide a large number of optional parameters, and the deep reinforcement learning algorithm can use a variety of parameters as input signals through a large amount of training to find the law between the data so that the algorithm has good performance. Therefore, it has specific applicability to deal with the instability of the multiple video stream environment in the field of video streaming. However, the DRL algorithm strongly depends on the training environment and training data. Machine learning algorithms, which train a large number of representative data, are often a more effective method. It is difficult for the existing machine learning methods to obtain enough representative training data in the related fields of video streaming. This is because the network tracking data used to simulate the Internet environment are collected from a single node in the network. However, the Internet itself is highly complex and diverse. What any single node observes is only a tiny part of the complex behavior of the network. Therefore, it is impossible to simulate the complex and diverse Internet [2]. For this reason, although the algorithm trained in the simulation environment has a good performance in the simulator, it is often unsatisfactory when deployed to the real network. In response to this problem, [PERSON] et al. [3] trained the algorithm by using a large amount of telemetry data of real video clients collected from video websites, so the algorithm also has good performance when extended to the real network.