Feed the output of a CNN in a LSTM
If you merge several small sequences from different videos to form a batch, the output of the last layer of your model (the RNN) should already be [batch_size, window_size, num_classes]. Basically, you want to wrap your CNN with reshape layers which will concatenate the frames from each batch:

input -> [batch_size, window_size, nchannels, height, width]
reshape -> [batch_size * window_size, nchannels, height, width]
CNN -> [batch_size * window_size, feat_size]
reshape -> [batch_size, window_size, feats_size]
RNN -> [batch_size, window_size, num_outputs]  (assuming frame-wise predictions)

But this will take a lot of memory, so you can set batch size to 1, which is what you seem to be doing if I understood correctly. In this case you can spare the first reshape.
I'm not sure about the order of the axes above, but the general logic remains the same.
As a side note: if you plan on using Batch Normalization at some point, you may want to raise the batch size because consecutive frames from a single segment might not contain a lot of variety by themselves. Also give a double check on the batch normalization axes which should cover both time and batch axes.
It is the first time that I am working with the LSTM networks. I have a video with a frame rate of 30 fps. I have a CNN network (AlexNet based) and I want to feed the last layer of my CNN network into the recurrent network (I am using tensorflow). Supposing that my batch_size=30, so equal to the fps, and I want to have a timestep of 1 second (so, every 30 frames). The output of the last layer of my network will be [bast_size, 1000], so in my case [30, 1000], now do I have to reshape the size of my output to [batch_size, time_steps, features] (in my case: [30, 30, 1000])? Is that correct? or am I wrong?