  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.
