This chapter explores the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in adaptive video quality optimization for large-scale streaming platforms. With the growing demand for high-quality, uninterrupted video streaming, deep learning has emerged as a powerful tool to improve Quality of Experience (QoE) by dynamically adjusting video quality in real time. Traditional rule-based adaptive bitrate (ABR) algorithms are often inadequate in dealing with fluctuating network conditions, leading to suboptimal user experiences. In contrast, deep learning models are capable of analyzing both video content and network conditions to optimize video quality, reduce latency, and personalize the streaming experience for individual users. This chapter highlights how CNNs process spatial information within video frames to optimize visual quality, while RNNs address temporal aspects by predicting network performance and adjusting quality accordingly. Several case studies are also examined, showcasing the effectiveness of deep learning in minimizing buffering and improving overall QoE in various streaming scenarios. Through detailed exploration of architecture, applications, and real-world implementations, this chapter provides a comprehensive understanding of how deep learning can transform the video streaming landscape.