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Feedback signaling plays a key role in flow control because the traffic source relies on the signaling information to make correct and timely flow-control decisions. Design of an efficient signaling algorithm is a challenging task since the signaling messages can tolerate neither error nor latency. Multicast flow-control signaling imposes two additional challenges: scalability and feedback synchronization. Previous research on multicast feedback-synchronization signaling has mainly focused on algorithm design and implementation. However, the delay properties of these algorithms are, despite their vital importance, neither well understood nor thoroughly studied. We develop both deterministic and statistical binary-tree models to study the delay performance of the multicast signaling algorithms. The deterministic model is used to derive the expressions of each path's feedback roundtrip time in a multicast tree, while the statistical model is employed to derive the general probability distributions of each path becoming the multicast-tree bottleneck. Using these models, we analyze and contrast the signaling delay scalability of two representative multicast signaling protocols - the soft-synchronization protocol (SSP) and the hop-by-hop (HBH) scheme - by deriving the first and second moments of multicast signaling delays. Also derived is the optimal flow-control update interval for SSP to minimize the multicast signaling delay.