End-to-end　learning-based　video　coding　has　attracted　substantial　attentions　by　compressing　video　signals　as　stacked　visual　features.　This　paper　proposes　an　end-to-end　deep　video　codec　with　jointly　optimized　compression　and　enhancement　modules　(JCEVC).　First,　we　propose　a　dual-path　generative　adversarial　network　(DPEG)　to　reconstruct　video　details　after　compression.　An　α-path　and　a　β-path　concurrently　reconstruct　the　structure　information　and　local　textures.　Second,　we　reuse　the　DPEG　network　in　both　motion　compensation　and　quality　enhancement　modules,　which　are　further　combined　with　other　necessary　modules　to　formulate　our　JCEVC　framework.　Third,　we　employ　a　joint　training　of　deep　video　compression　and　enhancement　that　further　improves　the　rate-distortion　(RD)　performance　of　compression.　Compared　with　x265　LDP　very　fast　mode,　our　JCEVC　reduces　the　average　bit-per-pixel　(bpp)　by　39.39%/54.92%　at　the　same　PSNR/MS-SSIM,　which　outperforms　the　state-of-the-art　deep　video　codecs　by　a　considerable　margin.　Sourcecode　is　available　at:　https://github.com/fwz1021/JCEVC.　©　2022　ACM.