Abstract: A software streaming platform may be implemented that predictively chooses units of a program to download based on the value of downloading the unit. In one example, a program is divided into blocks. The sequence in which blocks of the program historically have been requested is analyzed in order to determine, for a given history, what block is the next most likely to be requested. Blocks then may be combined into chunks, where each chunk represents a chain of blocks that have a high likelihood of occurring in a sequence. A table is then constructed indicating, for a given chunk, the chunks that are most likely to follow the given chunk. Based on the likelihood table and various other considerations, the value of downloading particular chunks is determined, and the chunk with the highest expected value is downloaded.

Abstract: A software streaming platform may be implemented that predictively chooses units of a program to download based on the value of downloading the unit. In one example, a program is divided into blocks. The sequence in which blocks of the program historically have been requested is analyzed in order to determine, for a given history, what block is the next most likely to be requested. Blocks then may be combined into chunks, where each chunk represents a chain of blocks that have a high likelihood of occurring in a sequence. A table is then constructed indicating, for a given chunk, the chunks that are most likely to follow the given chunk. Based on the likelihood table and various other considerations, the value of downloading particular chunks is determined, and the chunk with the highest expected value is downloaded.