Transform coefficient coding is a key module in modern video compression systems. Typically, a block of the quantized coefficients are processed in a pre-defined zig-zag order, starting from DC and sweeping through low frequency positions to high frequency ones. Correlation between magnitudes of adjacent coefficients is exploited via context based probability models to improve compression efficiency. Such scheme is premised on the assumption that spatial transforms compact energy towards lower frequency coefficients, and the scan pattern that follows a descending order of the likelihood of coefficients being non-zero provides more accurate probability modeling. However, a pre-defined zig-zag pattern that is agnostic to signal statistics may not be optimal. This work proposes an adaptive approach to generate scan pattern dynamically. Unlike prior attempts that directly sort a 2-D array of coefficient positions according to the appearance frequency of non-zero levels only, the proposed scheme employs a topological sort that also fully accounts for the spatial constraints due to the context dependency in entropy coding. A streamlined framework is designed for processing both intra and inter prediction residuals. This generic approach is experimentally shown to provide consistent coding performance gains across a wide range of test settings.