Minimum eigenvalue as a lower bound for strict convexity parameter

Question

Say $ f(x) = x A x^T $ is a strictly convex function. How can I prove that I can obtain a lower bound on the strictly convexity parameter of the function, if I know the minimum eigenvalue of the matrix $A$, where $A$ is the matrix associated with the quadratic form. So $\mu \geq \lambda_{\min}(A)$, where $\mu$ is the strictly convexity parameter.