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In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantization (R-Q) model-based H.264/AVC macroblock layer rate control for low bit rate real-time applications. The context-adaptive prediction scheme simultaneously exploits both spatial correlations and temporal correlations among the neighboring macroblocks within a so-called context of a macroblock. The location and shape of the context as well as the number of neighboring macroblocks in the context are adaptively computed according to local video signal characteristics using a Manhattan distance metric and an improved 2-D sliding window method. The proposed context-adaptive model parameter prediction scheme effectively suppresses the detrimental oscillations of estimated model parameters. Extensive experiments show that compared to the recent H.264/AVC reference software, macroblock layer rate control algorithm using our proposed context-adaptive prediction scheme significantly improves the MAD and model parameter prediction accuracy and bit achievement accuracy, and hence obtains much better rate distortion performance.