I assume your input data is image frames and your convolutions are 2D.
If your data is of shape (batch, seq, image_channels, height, width), then I you will need the CNN to treat the seq as an additional batch dimension. This sounds hard to achieve but there is an easy alternative: combine the batch and seq dimensions before feeding the data to the CNN. Something like this will do.
cnn_input = input.view(batch_size*seq_len, channels, height, width)

Then you can separate the batch and seq dimensions of the cnn output before feeding the data to the LSTM.
lstm_input = cnn_output.view(batch_size, seq_len, cnn_output.size(1), cnn_output.size(2), cnn_output.size(3))