Rate Adaptation

Rate adaptation, the dynamic adjustment of data transmission rates to optimize performance, is a crucial area of research across diverse fields. Current efforts focus on developing adaptive algorithms, often leveraging deep reinforcement learning or game theory, to optimize parameters like learning rates in machine learning or bitrates in video streaming, balancing quality and resource consumption. These advancements are improving efficiency in applications ranging from wireless video delivery and point cloud compression to federated learning, where minimizing communication overhead is critical for scalability. The ultimate goal is to achieve robust and efficient systems that adapt seamlessly to changing conditions and resource constraints.