## 1 Introduction  
The high penetration rate of smartphones and the development of 4G, 5G, and wireless local area network (WLAN)-based communication technologies have enabled users to experience high communication performance without restrictions on location or time. On the basis of these advancements in the network environment, demand for various services such as streaming, online games, and video conferencing has increased. In particular, the demand for video streaming is growing rapidly, leading to a significant increase in video streaming traffic within networks, as shown in Figure 1[1]. Figure 1 depicts the traffic volume ratios for each app in 2022. Video traffic volume accounts for approximately 65% of the total. Consequently, the need for efficient transmission of media content has increased, prompting several studies to address this issue [2]. To provide stable and efficient video streaming, service providers split videos into short chunks, encode each chunk at different quality levels, and store them on the streaming server. When a user requests a streaming service, the provider uses an adaptive bitrate (ABR) algorithm to deliver the video according to the user's network environment [3]. The ABR algorithm assesses the user's network conditions to ensure continuous video playback. Based on this assessment, chunks of appropriate video quality are selected and transmitted. Network performance needs to be measured to enable the ABR algorithm to select video chunks that match the network environment. An incorrect assessment of network performance may lead to the selection of high-quality video chunks that exceed the network's actual bandwidth; consequently, rebuffering may occur, causing video playback to stop despite the high video quality. Conversely, if only low-quality video chunks are selected, despite the network being capable of supporting higher quality, the streaming quality will be low, reducing user satisfaction, even though rebuffering does not occur. An ABR algorithm uses the network bandwidth observed while downloading the previous chunk to predict the bandwidth for downloading the next chunk. This approach works well in stable network environments, such as wired networks, where bandwidth remains consistent. However, in wireless environments, where network bandwidth can fluctuate significantly over time under varying channel conditions, this method reduces the accuracy of network performance prediction. When the accuracy of network performance prediction is low, it becomes challenging to provide optimal video streaming services, resulting in poor video quality despite buffering or frequent rebuffering. The buffer-based ABR (BBA) algorithm [4] was proposed to address this issue. The BBA selects the video quality for the next chunk by considering both the current playback buffer occupancy and network environment, aiming to prevent rebuffering and provide optimal video quality under varying network conditions. If the buffer occupancy is low, then a lower video quality is selected to increase the buffer, whereas a higher video quality is chosen when the buffer occupancy is high. However, BBA determines the next chunk's video quality based on the measured network bandwidth and buffer occupancy, and network bandwidth is measured based on the time taken to download the previous chunk and its size. Therefore, the algorithm may struggle to select the appropriate video quality if the network environment changes before the next chunk is downloaded. In wireless network environments, where communication conditions can fluctuate rapidly as a result of user mobility, BBA may select chunks with incorrect video quality, leading to rebuffering or  
Figure 1: Total volume of app categories in 2022.  
the selection of low-quality chunks, thereby degrading the user's quality of experience (QoE). Consequently, less rebuffering, less video rate variations, and high-rate video chunk selection are needed to increase QoE. Several studies have been conducted to increase the QoE of video streaming service users.