As　video　data　are　occupying　an　increasingly　more　significant　portion　of　global　data　traffic,　video　communication　has　become　an　indispensable　component　for　most　multimedia　applications.　As　an　enabling　technology　of　video　communication,　although　video　coding　is　well　standardized,　the　strategy　to　control　video　codec　is　highly　customized　to　applications.　The　consistency　of　video　quality　is　being　paid　more　attention　in　many　emerging　applications.　For　example,　in　video　surveillance,　the　quality　of　video　frames　should　he　stable　in　order　to　ensure　the　performance　of　machine　intelligence　algorithm,　such　as　object　detection　precision.　In　this　paper,　we　focus　on　achieving　constant　objective　reconstruction　quality　in　the　process　of　video　coding.　To　achieve　certain　rate-distortion　performance,　bit　rate　and　distortion　metric　are　usually　modeled　as　a　function　of　video　content　and　control　parameters　of　codec.　For　content　modeling　in　existing　work,　there　is　still　room　for　improvement,　including　the　design　of　more　efficient　content　feature,　the　compensation　for　assumption　about　constant　RD　characteristics　among　consecutive　frames,　and　the　adjustment　of　Lagrangian　multiplier　lambda　according　to　content　property.　The　main　contributions　of　this　paper　are:　1)　a　robust　content　adaptive　model　for　residual　bit　rate　modeling　based　on　content　statistics　called　mean　absolute　partial　transformed　difference　(MAPTD);　2)　a　content-related　header　hit　rate　modeling;　3)　preprocessing　scheme　for　robust　content　feature　estimation　at　scene　change;　4)　a　distortion　model　consistent　with　local　content;　and　5)　content　adaptive　lambda　determination.　The　experimental　results　show　that　our　constant　quality　control　strategy　can　achieve　superior　performance　compared　with　the　state-of-the-art　algorithms.