Quantum Machine Learning Revolution: Optimizing Adaptive Video Streaming Through the Power of Quantum Computing

As the demand for high-quality video content continues to soar, optimizing adaptive video streaming algorithms becomes increasingly crucial. This review paper explores the intersection of quantum machine learning (QML) and adaptive video streaming, delving into the theoretical foundations and potential advantages of leveraging quantum computing for streamlining content delivery. We elucidate the fundamental concepts of quantum computing, emphasizing the unique capabilities of quantum bits (qubits) in comparison to classical bits. Building upon classical machine learning limitations in video streaming, we articulate the theoretical framework for quantum machine learning applications. Our exploration encompasses the potential advantages of quantum computing, including parallelism, superposition, and entanglement, offering insights into how these features can revolutionize streaming optimization. We scrutinize the distinctive capabilities of quantum computing, such as Quantum Fourier Transform and Quantum Annealing, in enhancing signal processing and solving optimization problems within the context of adaptive streaming. The paper further investigates quantum-assisted streaming algorithms and bandwidth allocation strategies, providing a glimpse into the potential applications of quantum machine learning in the realm of adaptive video streaming. While outlining these advancements, we acknowledge the challenges posed by quantum error correction and the practical implementation hurdles. Lastly, we discuss the future prospects of quantum computing in video streaming, considering emerging technologies and addressing integration challenges. This review aims to provide a comprehensive understanding of the potential transformative impact of quantum machine learning on adaptive video streaming and sets the stage for future research in this rapidly evolving field