Rate Control for Video Streaming System with Neural Codecs

This paper introduces an end-to-end video transmission system that integrates a neural video codec and a novel rate control algorithm combined with a QoE model. The proposed algorithm dynamically adjusts quantization parameters in response to real-time network conditions and optimize QoE. Experimental results demonstrate that the proposed rate control algorithm significantly improves bitrate allocation, achieving a balanced trade-off between video quality and computational latency. Compared to traditional rate control methods, the system improves QoE by 56%.