Recurrent Neural Net (LSTM) batch size and input
Batch sizes are different to series length. Batch size refers to the number of observations, and are typically defined when one is performing mini batch stochastic gradient descent. 

If you are doing N mini batch SGD, you need to ensure the inputs to your network are a N x 168 tensor. Batch sizes are normally 32-128 observations.

WRT your second question, the approach is fine, it's just different. See which works better.