  We consider UAV IoT aerial sensing that delivers multiple VR/AR immersive
communication sessions to remote users. The UAV swarm is spatially distributed
over a wide area of interest, and each UAV captures a viewpoint of the scene
below it. The remote users are interested in visual immersive navigation of
specific subareas/scenes of interest, reconstructed on their respective VR/AR
devices from the captured data. The reconstruction quality of the immersive
scene representations at the users will depend on the sampling/sensing rates
associated with each UAV. There is a limit on the aggregate amount of data that
the UAV swarm can sample and send towards the users, stemming from
physical/transmission capacity constraints. Similarly, each VR/AR application
has minimum reconstruction quality requirements for its own session. We propose
an optimization framework that makes three contributions in this context.
First, we select the optimal sampling rates to be used by each UAV, such that
the system and application constraints are not exceed, while the priority
weighted reconstruction quality across all VR/AR sessions is maximized. Then,
we design an optimal scalable source-channel signal representation that
instills into the captured data inherent rate adaptivity, unequal error
protection, and minimum required redundancy. Finally, the UAV transmission
efficiency is enhanced by the use of small-form-factor multi-beam directional
antennas and optimal power/link scheduling across the scalable signal
representation layers. Our experiments demonstrate competitive advantages over
conventional methods for visual sensing. This is a first-of-its-kind study of
an emerging application of prospectively broad societal impact.
