Does overfitting depend only on validation loss or both training and validation loss?
In my opinion, only case 3 should be considered overfitting. As @stans has mentioned, there is not a very rigorous definition of overfitting so other people might think differently.
I wouldn't say the point where the validation loss stops decreasing is where bias and variance are minimized since there is a trade-off between bias and variance:

A constant model will have very low variance, but very high bias.
An overfitting model will have very low bias, but very high variance.

The point where the validation loss starts increasing can be considered optimal in terms of the sum of squared bias and variance, that is, an optimum of the generalization error.