What is meant by optimal estimator and how to determine optimality?
So Least squares estimator is as it literally - the estimator which brings the mean square error to minimum. In the case of Gaussian white noise it has a simple and analytic solution. I recommend you develop it yourself, if your'e comfortable with matrices calculus it is not that hard.

You can generate tons of estimators by defining different cost functions. Other popular cost functions are l1-norm ($\sum |x_i -  y_i|$), likelihood ($P(y|x)$). 

It's hard to define "better" if the case isn't the same, For example, does the case of very high variance poisson process is the same as the same variance gaussian process? I believe it is not, and in addition one is non negative integer while the other is real (and you can use it for you advantage).