End-to-end learned video compression has achieved strong rate-distortion performance, but rate control remains underexplored, especially in target-bitrate-driven and budget-constrained scenarios. Existing methods mainly rely on explicit R-D-lambda modeling or feed-forward prediction, which may lack stable online adjustment when video content varies dynamically. We propose a feedback-driven rate control framework for learned video compression. First, we build a single-model multi-rate coding interface on top of a DCVC-style framework, enabling continuous bitrate control through the rate-distortion parameter lambda. Then, a log-domain PI/PID closed-loop controller updates lambda online according to the error between the target bitrate and the entropy-estimated bitrate, achieving stable target bitrate tracking. To further improve frame-level bit allocation under budget constraints, we introduce a dual-branch GRU-based adjustment controller that refines the base control signal using budget-state features and causal coding statistics. Experiments on UVG and HEVC show that the proposed PI/PID controller achieves average bitrate errors of 2.88% and 2.95% on DCVC and DCVC-TCM, respectively. With the proposed adjustment controller, the method further achieves average BD-rate reductions of 5.69% and 4.49%, while reducing the average bitrate errors to 2.13% and 2.24%. These results show that the proposed method provides a practical solution for learned video compression with both controllable bitrate and improved rate-distortion performance.

翻译：端到端学习型视频压缩已取得强大的率失真性能，但码率控制问题仍研究不足，尤其是在目标码率驱动和预算受限场景中。现有方法主要依赖显式R-D-lambda建模或前馈预测，在视频内容动态变化时缺乏稳定的在线调整能力。我们提出一种面向学习型视频压缩的反馈驱动码率控制框架。首先，在DCVC风格框架上构建单模型多码率编码接口，通过率失真参数lambda实现连续码率控制。其次，采用对数域PI/PID闭环控制器，根据目标码率与熵估计码率之间的误差在线更新lambda，实现稳定的目标码率追踪。为在预算约束下进一步优化帧级码率分配，我们引入基于双分支GRU的调整控制器，利用预算状态特征和因果编码统计信息对基础控制信号进行精炼。在UVG和HEVC上的实验表明，提出的PI/PID控制器在DCVC和DCVC-TCM上分别达到2.88%和2.95%的平均码率误差。结合所提出的调整控制器，该方法进一步实现5.69%和4.49%的平均BD率下降，同时将平均码率误差分别降低至2.13%和2.24%。这些结果表明，所提方法为学习型视频压缩提供了一种兼顾可控码率与率失真性能提升的实用解决方案。