In　this　paper,　we　investigate　an　adaptive　channel　error-protection　scheme　for　streaming　stored　MPEG-4　fine　granular　scalability　(FGS)　bitstreams　over　error-prone　environments.　The　human　eye　is　sensitive　to　the　variations　in　reconstructed　image　quality.　Therefore,　instead　of　using　only　the　minimal　average　frame　distortion　as　the　optimization　criterion,　we　propose　a　rate-distortion　(R-D)-based　bit-allocation　method　to　determine　the　source　coding　rate　and　the　channel　coding　rate　under　a　given　channel　condition　so　as　to　minimize　both　the　average　image　distortion　and　quality　variation.　Based　on　our　proposed　piecewise　model　of　the　MPEG-4　FGS　enhancement　layer,　a　balance　between　the　average　frame　quality　and　quality　variation　will　be　obtained.　Experimental　results　show　that,　compared　with　other　existing　methods,　our　proposed　method　can　achieve　less　quality　variation　and　comparable　average　frame　distortion　under　various　channel　conditions.