Choosing One vs All and One vs One for Multiclass SVM
The main consideration is the number of classes, assume you have $N$ different classes: "one vs all" will train one classifier per class, so $N$ classifiers in total. For a given class $c_i$ the classifier assumes samples with $c_i$ as positives and the rest as negatives. Obviously, this leads to imbalanced datasets, moreover, in "one vs all" the outputs must be calibrated to produce comparable results.

In "one vs one" you train a separate classifier for each different pair of classes, so $\frac{N(N - 1)}{2}$, obviously this is computationally expensive. 

For more details see this: 1 and 2.