  The support of coexisting ultra-reliable and low-latency (URLL) and enhanced
Mobile BroadBand (eMBB) services is a key challenge for the current and future
wireless communication networks. Those two types of services introduce strict,
and in some time conflicting, resource allocation requirements that may result
in a power-struggle between reliability, latency, and resource utilization in
wireless networks. The difficulty in addressing that challenge could be traced
back to the predominant reactive approach in allocating the wireless resources.
This allocation operation is carried out based on received service requests and
global network statistics, which may not incorporate a sense of
\textit{proaction}. Therefore, this paper proposes a novel framework termed
\textit{service identification} to develop novel proactive resource allocation
algorithms. The developed framework is based on visual data (captured for
example by RGB cameras) and deep learning (e.g., deep neural networks). The
ultimate objective of this framework is to equip future wireless networks with
the ability to analyze user behavior, anticipate incoming services, and perform
proactive resource allocation. To demonstrate the potential of the proposed
framework, a wireless network scenario with two coexisting URLL and eMBB
services is considered, and two deep learning algorithms are designed to
utilize RGB video frames and predict incoming service type and its request
time. An evaluation dataset based on the considered scenario is developed and
used to evaluate the performance of the two algorithms. The results confirm the
anticipated value of proaction to wireless networks; the proposed models enable
efficient network performance ensuring more than $85\%$ utilization of the
network resources at $\sim 98\%$ reliability. This highlights a promising
direction for the future vision-aided wireless communication networks.
