  In Part I of this paper, we proposed and analyzed a novel algorithmic
framework for the minimization of a nonconvex (smooth) objective function,
subject to nonconvex constraints, based on inner convex approximations. This
Part II is devoted to the application of the framework to some resource
allocation problems in communication networks. In particular, we consider two
non-trivial case-study applications, namely: (generalizations of) i) the rate
profile maximization in MIMO interference broadcast networks; and the ii) the
max-min fair multicast multigroup beamforming problem in a multi-cell
environment. We develop a new class of algorithms enjoying the following
distinctive features: i) they are \emph{distributed} across the base stations
(with limited signaling) and lead to subproblems whose solutions are computable
in closed form; and ii) differently from current relaxation-based schemes
(e.g., semidefinite relaxation), they are proved to always converge to
d-stationary solutions of the aforementioned class of nonconvex problems.
Numerical results show that the proposed (distributed) schemes achieve larger
worst-case rates (resp. signal-to-noise interference ratios) than
state-of-the-art centralized ones while having comparable computational
complexity.
