## 1 Introduction  
Maximizing the quality of experience (QoE) for users of video streaming services over wireless networks is a prominent challenge for researchers and video providers. Multimedia content, particularly videos, occupies a substantial portion of internet traffic [1], making it of utmost importance to address this challenge. The dynamic adaptive streaming over HTTP (DASH) standard plays a vital role in selecting the appropriate quality of video segments based on network conditions, even over networks with limited bandwidth. However, DASH streaming can be susceptible to quality variations and rebuffering events, especially in challenging network conditions. To enhance user QoE [2], service providers and academic researchers have proposed several approaches aimed at achieving specific objectives, such as minimizing initial delays, preventing interruptions, and ensuring consistently high quality throughout streaming sessions. In the DASH server, videos are divided into fixed-duration chunks or segments, typically ranging from 2 to 10 s. These segments are encoded in different qualities, and their details, including URLs, are stored in a Media Presentation Description (MPD) file. The DASH client is responsible for deciding the quality of the next segment to be downloaded, taking into consideration factors such as current network conditions, buffer occupancy, and device capabilities [3]. Furthermore, during a streaming session, the segments can be classified into four states: played, playing, in buffer, and to be downloaded. The buffer can contain segments of varying qualities, including low, medium, and high. The design of an optimal adaptive bitrate (ABR) algorithm for dynamic adaptive streaming over HTTP (DASH) video streaming in highly dynamic environments presentsa significant challenge [4]. On one hand, fixed-rule-based ABR algorithms struggle to adapt to the varying conditions encountered during streaming. To address this, many ABR schemes have integrated artificial intelligence (AI) technologies to enhance user QoE. However, ensuring high user QoE in diverse network environments and client equipment can be challenging. These ABR schemes need to improve perceived QoE, minimize quality switches, prevent interruptions, and reduce initial delays. On the other hand, artificial intelligence technology, especially deep reinforcement learning (DRL), indeed provides robust techniques, with promising applications in various domains [5; 6; 7; 8; 9], including video streaming. In this subsection, we have identified several potential challenges associated with applying DRL in the context of DASH:  
* Optimizing QoE: One significant challenge lies in how DRL effectively manages the trade-off between delivering high-quality video and ensuring a seamless streaming experience. * Adaptation to Dynamic Network Conditions: DASH operates in environments characterized by constantly changing network parameters, such as bandwidth and latency. It is essential to explore how DRL models adapt to these dynamic fluctuations and make real-time decisions to maintain optimal streaming quality. * Segmentation and Quality Switching: The process of segmenting video content and determining when and how to switch between different quality levels is intricate. Discussing these intricacies sheds light on the challenges involved in ensuring a smooth transition between video segments. * Reward Function Design: Designing a reward function that accurately reflects the quality of the streaming experience and encourages optimal decision making is a complex task. Elaborating on this complexity can provide insights into the inner workings of DRL models. * Training Complexity: Training DRL models for video streaming poses computational challenges. This includes the need for substantial datasets and considerable computational resources. Exploring these training complexities highlights the practical considerations involved in implementing DRL for video streaming applications.