BAR: Blockwise Adaptive Recoding for Batched Network Coding

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time.Comment: submitted for journal publicatio

Saving New Sounds

"Over seventy-five million Americans listen to podcasts every month, and the average weekly listener spends over six hours tuning into podcasts from the more than thirty million podcast episodes currently available. Yet despite the excitement over podcasting, the sounds of podcasting’s nascent history are vulnerable and they remain mystifyingly difficult to research and preserve. Podcast feeds end abruptly, cease to be maintained, or become housed in proprietary databases, which are difficult to search with any rigor. Podcasts might seem to be highly available everywhere, but it’s necessary to preserve and analyze these resources now, or scholars will find themselves writing, researching, and thinking about a past they can’t fully see or hear. This collection gathers the expertise of leading and emerging scholars in podcasting and digital audio in order to take stock of podcasting’s recent history and imagine future directions for the format. Essays trace some of the less amplified histories of the format and offer discussions of some of the hurdles podcasting faces nearly twenty years into its existence. Using their experiences building and using the PodcastRE database—one of the largest publicly accessible databases for searching and researching podcasts—the volume editors and contributors reflect on how they, as media historians and cultural researchers, can best preserve podcasting’s booming audio cultures and the countless voices and perspectives podcasting adds to our collective soundscape.

Expanding-window BATS code for scalable video multicasting over erasure networks

In this paper we consider scalable video multicasting over erasure networks with heterogeneous video quality requirements. With random linear network coding (RLNC) applied at the intermediate nodes the information received by the destinations is determined by the associated channel rank distributions based on which we obtain the optimal achievable code rate at the source node. We show that although a concatenation of priority encoded transmission (PET) with RLNC achieves the optimal code rate it incurs prohibitive high coding complexity. On the other hand batched sparse (BATS) code has been recently proposed for unicast networks which has low coding complexity with near-optimal overhead. However the existing BATS code design cannot be applied for multicast networks with heterogeneous channel rank distributions at different destinations. To this end we propose a novel expanding window BATS (EW-BATS) code where the input symbols are grouped into overlapped windows according to their importance levels. The more important symbols are encoded with lower rate and hence they can be decoded by more destinations while the less important symbols are encoded with higher rate and are only decoded by the destinations with high throughput for video quality enhancement. Based on asymptotical performance analysis we formulate the linear optimization problems to jointly optimize the degree distributions for each window and the window selection probabilities. Simulation results show that the proposed EW-BATS code satisfies the decoding requirements with much lower transmission overhead compared with separate BATS code where the degree distributions are separately optimized for each destination