What is "Maximum Quantization Error"?
When you quantize a signal, you introduce and error which can be defined as 
$$q[n] = x_q[n]-x[n]$$
where $q[n]$ is the quantization error, $x[n]$ the original signal, and $x_q[n]$ of the quantized signal. The maximum quantization error is simply $max(\left | q \right |)$, the absolute maximum of this error function. Dx in this definition seems to be the range of the input signal so we could rewrite this as
$$Q = \frac{max(x)-min(x)}{2^{N+1}}$$

Let's look at a quick example. Let's assume you have a signal that's uniformly distributed between -1 and +1 and you want to quantize this with 3 bits. You have a total 8 of quantizaton steps which would map to [-1 -.75 -.5 -25 0 .25 .5 .75]. The difference between steps is 0.25. If you round during quantization the maximum error will be half of that (i.e. 0.125). 

Now let's try the formula:
$$Q = \frac{max(x)-min(x)}{2^{N+1}} = \frac{1-(-1)}{2^{3+1}}= \frac{2}{16} = 0.125$$