No algorithm is known to solve this in the same manner or with the same efficiency as conventional Huffman coding.

Huffman coding: this process replaces fixed length symbols in the range 0-258 with variable length codes based on the frequency of use.

Further compression is achieved by entropy coding using Huffman coding of the various bitstream elements that result from the process above.

This is the final step in the video encoding process, so the result of Huffman coding is known as the MPEG-1 video "bitstream."

Unlike Layers I/II, MP3 uses variable-length Huffman coding (after perceptual) to further reduce the bitrate, without any further quality loss.

Huffman coding: this is approximately the opposite of LZW, and generates a set of variable length codes for fixed length data items, using the shortest codes for most common data items.

Length-limited Huffman coding is a variant where the goal is still to achieve a minimum weighted path length, but there is an additional restriction that the length of each codeword must be less than a given constant.

On-line textbook: Information Theory, Inference, and Learning Algorithms, by David MacKay, gives an accessible introduction to Shannon theory and data compression, including the Huffman coding and arithmetic coding.

Adaptive Huffman coding (also called Dynamic Huffman coding) is an adaptive coding technique based on Huffman coding.

"Blocking", or expanding the alphabet size by grouping multiple symbols into "words" of fixed or variable-length before Huffman coding helps both to reduce that inefficiency and to take advantage of statistical dependencies between input symbols within the group (as in the case of natural language text).