## 3 Related Work  
Numerous studies have been conducted to improve the QoE in video streaming. Approaches have been proposed for adjusting the bitrate in real time in response to network conditions acquired through reinforcement learning [5; 9; 10]. [PERSON] et al. [9] employed reinforcement learning to optimize the heuristic-based adjustment coefficient function \(F(\bullet)\) to enhance the user QoE. In contrast, we focus on the correlation between buffer occupancy and network bandwidth rather than adjustment coefficients. [PERSON] et al. [5] introduced a widely recognized play buffer-based ABR algorithm that improves user QoE by applying the Lyapunov optimization technique to the relationship between the network environment and playback buffer status. Although that study optimized the correlation between the playback buffer and network environment, we focus on predicting the network bandwidth, which is a different approach. [PERSON] et al. [10] proposed a hybrid method that combines a buffer-based ABR algorithm with deep learning technology to improve QoE by selecting videos with the optimal quality. Proximal policy optimization for ABR (PPO-ABR) [13] employs a deep reinforcement learning technique to implement real-time bitrate optimization strategy for maximizing QoE. PPO-ABR selects the best policy based on the current network conditions. These studies utilized methods for predicting network conditions, buffer conditions, and user QoE, and for selecting policies through reinforcement learning. By contrast, our proposed scheme uses a GRU rather than reinforcement learning to predict the network bandwidth.  
In [20], a bidirectional long short-term memory convolutional neural network (BiLSTM-CNN), LSTM, BiLSTM, support vector regression, and multilayer perceptron (MLP) models were used to predict the future QoE of users. The LL-GABR model proposed in [21] predicts user QoE by inputting video quality, latency, rebuffering, and energy consumption into a reinforcement learning framework. These studies employ a variety of deep learning and machine learning techniques to predict the user QoE, whereas we use a GRU to forecast the network bandwidth. In [22], network bandwidth is predicted using a bidirectional GRU (BiGRU). A novel adaptive bitrate selection algorithm (nABR) that selects the quality of the next video chunk based on the predicted bandwidth is proposed, and its performance is evaluated through numerical analysis. The GRU model and video chunk selection algo  
Figure 3: Video quality selection in the buffer-based adaptive bitrate algorithm.  
rithm proposed in this paper have a lower computational overhead, and the performance was evaluated using network bandwidth measurement data based on user mobility.  
[PERSON] et al. [23] selected the optimal video quality by considering buffer occupancy and estimated network conditions, based on real-time monitoring and network status analysis methods. They relied on conventional network performance indicators such as TCP segment transmission time, packet loss rate, and round-trip time, which differ from the time series network bandwidth prediction method employed in this study. In [24; 25], MEC capable of delivering cache services is proposed to improve QoE and provide low latency for video streaming applications. In [26], a proof-of-concept for a mobile video-streaming service utilizing MEC is developed. Although these studies improve the QoE of video streaming with MEC, we improve the QoE of streaming services in non-MEC scenarios.