Best of Both Worlds: Prioritizing Network Coding without Increased Space Complexity
Mathematisch-Naturwissenschaftliche Fakultät
Random linear network coding simplifies routing decisions, improves throughput, and increases tolerance against packet loss. A substantial limitation, however, is delay: decoding requires as many independent linear combinations as data blocks. Hierarchical network coding purportedly solves this delay problem. It introduces layers to decode prioritized data blocks early, which may benefit video streaming applications or applications for sensor information collection. While hierarchical network coding reduces decoding delays, it introduces significant space complexity and additional decoding time. We propose a decoding algorithm that manages all prioritization layers in a joint decoder matrix. Analytical evaluation and performance measurements show that we maintain prioritization benefits without increased space complexity and improve decoding performance. With memory requirements independent of the number of layers, our algorithm facilitates more fine-grained prioritization layers to further the benefits of hierarchical network coding.
