Performance Analysis of Reliable Video Streaming with Strict Playout Deadline in Multi-Hop Wireless Networks
This addresses the challenge of reliable video streaming for vision-based intelligent services in wireless networks, representing an incremental improvement with specific gains.
The paper tackled the problem of rate adaptation for high-quality, low-delay video streaming over multi-hop wireless networks by proposing a low-complexity, model-based algorithm using stochastic network calculus. It achieved less than 10% quality degradation compared to the best achievable performance in simulations.
Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high quality and low delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity, model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the model using extensive simulations. We show that it allows fast, near optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than $10$\% quality degradation compared to the best achievable performance.