  Edge intelligence is an emerging technology where the base stations located
at the edge of the network are equipped with computing units that provide
machine learning services to the end users. To provide high-quality services in
a cost-efficient way, the wireless and computing resources need to be
dimensioned carefully. In this paper, we address the problem of resource
dimensioning in a single-cell system that supports edge video analytics under
latency and accuracy constraints. We show that the resource-dimensioning
problem can be transformed into a convex optimization problem, and we provide
numerical results that give insights into the trade-offs between the wireless
and computing resources for varying cell sizes and for varying intensity of
incoming tasks. Overall, we observe that the wireless and computing resources
exhibit opposite trends; the wireless resources favor from smaller cells, where
high attenuation losses are avoided, and the computing resources favor from
larger cells, where statistical multiplexing allows for computing more tasks.
We also show that small cells with low loads have high per-request costs, even
when the wireless resources are increased to compensate for the low
multiplexing gain at the servers.
