  We propose an application-layer forward error correction (AL-FEC) code rate
allocation scheme to maximize the quality of experience (QoE) of a video
multicast. The allocation dynamically assigns multicast clients to the quality
layers of a scalable video bitstream, based on their heterogeneous channel
qualities and video playback capabilities. Normalized mean opinion score (NMOS)
is employed to value the client's quality of experience across various possible
adaptations of a multilayer video, coded using mixed spatial-temporal-amplitude
scalability. The scheme provides assurance of reception of the video layers
using fountain coding and effectively allocates coding rates across the layers
to maximize a multicast utility measure. An advantageous feature of the
proposed scheme is that the complexity of the optimization is independent of
the number of clients. Additionally, a convex formulation is proposed that
attains close to the best performance and offers a reliable alternative when
further reduction in computational complexity is desired. The optimization is
extended to perform suppression of QoE fluctuations for clients with marginal
channel qualities. The scheme offers a means to trade-off service utility for
the entire multicast group and clients with the worst channels. According to
the simulation results, the proposed optimization framework is robust against
source rate variations and limited amount of client feedback.
