## 1 Introduction
The network and buffer statuses were used to predict the bitrate of the following video chunk [4; 5; 6; 7]. However, although these strategies are effective in a stable network environment, their performance may suffer when the network fluctuates dramatically. To deal with network bandwidth change, research has been performed to improve the user QoE of video streaming services using deep learning and reinforcement learning [8; 9; 10; 11; 12; 13]. These studies, however, use reactive approaches that modify the bitrate in response to changes in the network state. Schemes that use mobile edge computing (MEC) have also been proposed to increase the real-time performance of video streaming [14; 15; 16; 17]. However, to use MEC, the existing streaming service protocol must be modified. This study proposes a gated recurrent unit (GRU)-based network performance prediction model and a buffer-based ABR algorithm that leverages the resulting predictions for efficient streaming service provisions, even in environments with significant network fluctuations. First, we use the GRU, which is a time-series model, to predict network performance at the time of the next chunk download, improving the accuracy of network performance predictions for video streaming users in wireless environments. The GRU, a type of recurrent neural network (RNN), is designed to solve the long-term dependency problem of long short-term memory (LSTM), while being more lightweight by reducing the computations required to update the hidden state [18]. The GRU demonstrated a quicker learning curve and better prediction performance than LSTM in [19]. The main components of the GRU are the update gate and reset gate, which combine input data and the previous hidden state to create a new hidden state. This model efficiently learns from long sequence data. Second, by selecting chunks with appropriate video quality based on the predicted network bandwidth and current playback buffer occupancy, we improve the user QoE by minimizing rebuffering and unnecessary video quality changes compared with the existing BBA. The proposed scheme was evaluated using the throughput of an actual long-term evaluation (LTE) network to evaluate realistic performance and reduce dataset dependencies. The QoE of user streaming services was measured in various scenarios, including walking, riding a bus, and traveling by train, indicating an average improvement of 11% compared with the existing BBA. In summary, the main contributions of this paper are as follows:  
* We propose a GRU based network bandwidth prediction model. * We select the rate of the next video chunk based on the predicted network bandwidth and buffer occupancy. * We evaluate and compare the QoE of the proposed scheme. Our scheme outperforms BBA in QoE by approximately 11%. The remainder of this paper is organized as follows: Section 2 reviews studies and background knowledge relevant to this research. Section 3 discusses related works. Section 4 introduces the GRU-based network performance prediction method and video quality selection algorithm based on playback buffer occupancy. Section 5 presents performance evaluation and discusses the results. Finally, Section 6 concludes this paper and outlines directions for future research.