wavelet packet transform and lifting scheme?
A lifting scheme is a method for splitting a sequence of discrete samples into downsampled subsequences, so that you can predict a subsequence from the other, and update the former later, using possibly nonlinear predict and update operators. This corresponds to a generalization of one level of discrete wavelet transform, usually obtained fully linearly by convolution (first-generation wavelet).

Lifting wavelets were called second-generation wavelets, and proved to be a generalization of first-generation wavelets.

This being said, for question 1:


yes, a wavelet packet being iterations of several one level DWT on other subbands. 


For question 2:


A lifting discrete wavelet packet can be  critically sampled, like the DWT, but also can be undecimated or oversampled if needed. And they are interesting questions about what kind of  stationary lifted wavelets can be built.


For question 3:

Generally, the Heisenberg boxes are considered to be of constant area. With the STFT, titles have the same shape, whereas with wavelet they are dilated/stretched in each direction. With a uniform wavelet packet decomposition (where the time-scale plot is regularly discretized), the time-scale bins can look similar to that of  the STFT. If the packet transform is critical, the result is often poorer with wavelets packets (because of the lack of redundnacy). 

At the same redundancy, aliasing and constraints on wavelet shapes often yield less degrees of freedom that full-fledged oversampled filter banks, but the filter are generally easier to optimize. Nevertheless, if you want uniform frequency bin, wavelets are rarely very beneficial.