Adaptive source and channel coding for distributed applications

Distributed applications are often faced with a choice between improved throughput or improved reliability but not both. We argue that this is not a strict dichotomy and propose a framework that improves both application performance and reliability by adaptively adjusting source and channel coding parameters. For simplicity, we assume that the source and channel we work with are memoryless and in general behave in a way such that Shannon´s separation theorem holds. Although not all sources and channels could be characterized as such, doing so allows us to work this resource allocation problem in parallel. We reduce redundant transmissions and minimize bandwidth utilization through an LZ77 style dictionary based source coding approach. To ensure data integrity, we apply rateless forward error correction techniques at the transport layer. Our algorithm works in conjunction with physical layer forward error correction and generates just enough overhead needed to achieve error free transmission without requiring a heavy use of a reverse channel for acknowledgments. We show through simulations that our combined source and channel approach reduces network traffic in our experimental platform by a measurable amount while maintaining and at times exceeding the Quality of Service (QoS) that is obtained without our technique.