Deep　video　coding　has　paved　a　way　to　break　through　the　performance　bottleneck　of　reigning　hybrid　video　coding.　However,　unlike　hybrid　video　codecs,　existing　deep　video　codecs　cannot　offer　both　flexible　rates　and　regulable　complexities　within　one　single　codec,　which　limits　their　applications.　In　this　paper,　we　propose　a　Regulable　Deep　Video　Codec　(RDVC)　to　address　the　above　issue.　First,　we　propose　an　Adaptive　Feature　Compression　(AFC)　network　that　generates　variable　rates　while　ensuring　Rate-Distortion　(RD)　performance.　The　network　introduces　a　two-stage　coarse-to-fine　rate　adjustment　that　can　be　controlled　by　a　user-specified　rate　level.　Second,　we　propose　a　Spatio-Temporal　Feature　Propagation　(STFP)　mechanism　to　provide　high-quality　reference　information　for　AFC　process.　Third,　we　also　utilize　slimmable　convolutional　components　in　our　framework　to　adjust　decoding　complexity　constrained　by　user　configuration.　Experimental　results　demonstrate　that　RDVC　can　adjust　the　codec　structure　flexibly　according　to　different　user　configurations　while　maintaining　advanced　performance.　On　average,　it　reduces　the　bit-per-pixel　(bpp)　by　9.35%/58.12%　while　maintaining　the　same　PSNR/MS-SSIM　as　the　reference　software　VTM-13.2.　Sourcecode　is　available　at:　https://github.com/WXJ0001/RDVC.　　©　2025　IEEE.