Edge computing springs up a modern computing platform for Internet of Things (IoT), smart systems, and multimedia applications. These technologies are built using resource-constrained devices, which are incapable of executing complex tasks. Edge computing offers computation offloading to make them capable, but offloading at large scale creates congestion, and originate scalability problem in edge computing. This study focuses on addressing scalability issue by proposing a state-of-the-art cross-entropy based scalable edge computing framework. The framework comprises over IoT devices, the edge servers, and the cloud. We have clustered the IoT devices using social IoT (SIoT) clustering technique for control and improved QoS. We propose a cross entropy-based latency-critical computation offloading algorithm (LACCoA) for efficient resource scheduling at edge layer. It makes use of Kullback-Leibler (K-L) divergence, which is a distance metric between two probability distributions. LACCoA ensures the parallel utilization of edge resources, hence producing solutions with low computational complexity. In addition with, a lightweight request and admission cycle which ensure seamless computation offloading process. The abovementioned technique produces desirable results compared to particle swarm optimization (PSO) and adaptive PSO. The experimental results showed notable improvement in reducing latency, minimizing energy consumption, and converge the QoS requirements of the multimedia application and IoT. Furthermore, the framework also scale the edge server to compute the maximum number of offloaded tasks.