Simulation of FEC-Based Error Control for Packet Audio on the Internet

Real-time audio over a best-effort network, such as the Internet, frequently suffers from packet loss. To mitigate the impact of such packet loss, several research efforts [1, 2] and implementation studies [3] advocate the use of forward error correction (FEC) coding. Although these prior works have pioneered promising and novel applications of FEC to Internet audio, they do not definitively demonstrate the advantages of FEC because they do not evaluate aggregate performance that results from multiplexing many like flows. For example, [1] assesses the efficacy of FEC by generalizing the performance of a single audio connection; [3] restricts its design space to one particular coding scheme and does not evaluate the potentially negative impact of increased network load that results from FEC; and [2] limits its scope to an allaudio source network employing a simplistic FEC-encoding. In this paper, we build on these landmark works with a systematic study of FEC for packet audio that characterizes the aggregate performance across all audio sources in the network. We refine the novel but ad hoc coding techniques proposed in [3] into a formal framework that we call “signal processing-based FEC” (SFEC) and use our framework to more rigorously evaluate the relative merits of this approach. Through extensive simulation, we evaluate the “scalability” of SFEC for packet audio — i.e., the ability for a coding algorithm to improve aggregate performance when used by all sources in the network — and find that optimal signal quality is achieved when sources react to network congestion not by blindly adding FEC, but rather by adding FEC in a controlled fashion that simultaneously constrains the sourcecoding rate. As a result, packet loss is mitigated without introducing more congestion, thus admitting a more scalable and effective approach than successively adding redundancy to a constant bit-rate source. While this result may seem intuitive, it has not been previously suggested in the context of Internet audio, and until now, has not been systematically studied.