  This work details a scalable framework to orchestrate a swarm of rotary-wing
UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity
and traffic offloading for ground users. First, a Multiscale Adaptive
Energy-conscious Scheduling and TRajectory Optimization (MAESTRO) framework is
developed for a single UAV. Aiming to minimize the time-averaged latency to
serve user requests, subject to an average UAV power constraint, it is shown
that the optimization problem can be cast as a semi-Markov decision process,
and exhibits a multiscale structure: outer actions on radial wait velocities
and terminal service positions minimize the long-term delay-power trade-off,
optimized via value iteration; given these outer actions, inner actions on
angular wait velocities and service trajectories minimize a short-term
delay-energy cost. A novel hierarchical competitive swarm optimization scheme
is developed in the inner optimization, to devise high-resolution trajectories
via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms
(MAESTRO-X) via scalable policy replication: enabled by a decentralized
command-and-control network, the optimal single-agent policy is augmented with
spread maximization, consensus-driven conflict resolution, adaptive frequency
reuse, and piggybacking. Numerical evaluations show that, for user requests of
10 Mbits, generated according to a Poisson arrival process with rate 0.2
req/min/UAV, single-agent MAESTRO offers 3.8x faster service than a
high-altitude platform and 29% faster than a static UAV deployment; moreover,
for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7x faster than
a successive convex approximation scheme; and remarkably, a single UAV
optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.
