Effects of the Generation Size and Overlap on Throughput and Complexity in Randomized Linear Network Coding

TL;DR

This paper analyzes how generation size and overlap in randomized linear network coding affect throughput and complexity, using a coupon collector model to quantify tradeoffs and demonstrate that overlaps can significantly improve throughput.

Contribution

It introduces a coupon collector-based model to analyze throughput loss and complexity tradeoffs in generation-based network coding with overlapping generations.

Findings

Increasing generation size quickly approaches link capacity.

Overlapping generations significantly improve throughput at small sizes.

The model quantifies the probability of decoding failure and expected packet requirements.

Abstract

To reduce computational complexity and delay in randomized network coded content distribution, and for some other practical reasons, coding is not performed simultaneously over all content blocks, but over much smaller, possibly overlapping subsets of these blocks, known as generations. A penalty of this strategy is throughput reduction. To analyze the throughput loss, we model coding over generations with random generation scheduling as a coupon collector's brotherhood problem. This model enables us to derive the expected number of coded packets needed for successful decoding of the entire content as well as the probability of decoding failure (the latter only when generations do not overlap) and further, to quantify the tradeoff between computational complexity and throughput. Interestingly, with a moderate increase in the generation size, throughput quickly approaches link capacity. Overlaps between generations can further improve throughput substantially for relatively small generation sizes.