svm scaling input values

I am using libSVM. Say my feature values are in the following format:

                         instance1 : f11, f12, f13, f14
                         instance2 : f21, f22, f23, f24
                         instance3 : f31, f32, f33, f34
                         instance4 : f41, f42, f43, f44
                         ..............................
                         instanceN : fN1, fN2, fN3, fN4

I think there are two scaling can be applied.

1. scale each instance vector such that each vector has zero mean and unit variance.

       ( (f11, f12, f13, f14) - mean((f11, f12, f13, f14) ). /std((f11, f12, f13, f14) )
   

2. scale each colum of the above matrix to a range. for example [-1, 1]

According to my experiments with RBF kernel (libSVM) I found that the second scaling (2) improves the results by about 10%. I did not understand the reason why (2) gives me a improved results.

Could anybody explain me what is the reason for applying scaling and why the second option gives me improved results?