Machine learning for ultra-low-latency video encoder and rate controller

Abstract

Ultra-low-latency video encoding and rate control methods and systems are configured to minimize latency in applications such as cloud gaming and real-time communication. The methods and systems enhance complexity estimation and buffer management. A dynamic rate controller architecture adjusts encoding based on complexity and buffer status, ensuring consistent bitrate and quality. Deep learning models predict optimal encoder instances and quantization parameter (QP) distributions, improving encoding efficiency. System architectures, pseudocode for bitrate and QP calculations, and training and testing phases for deep learning models are also provided.