## 1 Introduction
Existing approaches to DASH video streaming typically rely on heuristics or machine learning models to make bitrate selection decisions. However, these approaches often have limited performance and are not able to adapt to dynamic network conditions effectively. In this paper, we propose a novel ABR algorithm based on deep reinforcement learning for determining the quality of the next segment in DASH video streaming over wireless networks. Our approach takes into account three key parameters: network conditions, buffer state, and the distance factor between consecutive video segments. The primary objective of our proposal is to provide a high-quality user experience. The main contributions of our work are as follows:  
* Formulation and design model: We introduce a new deep reinforcement learning approach for DASH video streaming that focuses on controlling the quality distance between consecutive segments. By doing so, we effectively manage the perceptual quality switch. We formulate the DASH video streaming process within a learning model called the Markov Decision Process, enabling the determination of optimal solutions through reinforcement learning. * Optimizing QoE: To optimize the QoE, we define the QoE function that relies on three key parameters: the quality of each segment, the perceptual change in quality, and playback interruptions. * Reward Function Design: We have designed and formulated a reward function that takes into account the rate of perceived quality change, occurrences of rebuffering events, and the quality of each video segment. * Analysis and implementation: We classify the available qualities (bitrates) into three classes: Qhigh, Qmedium, and Qpoor. To evaluate our approach, we conducted experiments using the animation video sequence \"Big Buck Bunny\" in the DASH.jsenvironment over a wireless network. The experiments involved playing video sequences on different devices under various network conditions. We monitored the performance of the experiments throughout the streaming sessions, specifically observing the perceptible quality switch. Based on the observed quality switches, we classified the bitrates into three distinct classes. * Training process: To tackle the training challenge of DRL and achieve enhanced performance, we leveraged a comprehensive and diverse dataset, and we trainees the DRL agent using a two-step training process. * Simulation and comparison: We simulated and compared our proposed approach with existing studies. The results demonstrated a significant improvement in QoE, providing highly stable video quality. Our scheme successfully minimized the distance factor, ensuring a smooth streaming session. Our proposal enhances video QoE using deep reinforcement learning by employing the following techniques: Firstly, we categorize video quality into three distinct classes and define the distance factor between two consecutive video segments. Secondly, we treat client-side adaptation as an autonomous agent. This agent is represented by a deep neural network, which generates policies for making decisions. This network includes an LSTM neural network component responsible for determining the optimal quality for the next video segment and receiving rewards iteratively until all segments have been downloaded. The structure of this study's remaining sections is as follows: Section 2 offers an overview of related work, analyzing previous research in the field. Section 3 provides a comprehensive description of our proposed solution, delineating the methodology and approach used in the development of our innovative ABR algorithm based on deep reinforcement learning. In Section 4 showcases the experimental results derived from our system's evaluation, engaging in a discussion of the performance and outcomes of our approach. Finally, Section 5 concludes the paper by summarizing the key findings, emphasizing our work's contributions, and exploring potential avenues for future research in this domain.