Let’s divide the issues one by one:
About tensorflow to allocate all memory in advance, you can use following code snippet to let tensorflow allocate memory whenever it is needed. So that you can understand how the things are going.
gpu_options = tf.GPUOptions(allow_growth=True)
session = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=gpu_options))
This works equally with tf.Session() instead of tf.InteractiveSession() if you prefer.
Second thing about the sizes,
As there is no information about your network size, we cannot estimate what is going wrong. However, you can alternatively debug step by step all the network. For example, create a network only with one layer, get its output, create session and feed values once and visualize how much memory you consume. Iterate this debugging session until you see the point where you are going out of memory.
Please be aware that 3840 x 155229 output is really, REALLY a big output. It means ~600M neurons, and ~2.22GB per one layer only. If you have any similar size layers, all of them will add up to fill your GPU memory pretty fast.
Also, this is only for forward direction, if you are using this layer for training, the back propagation and layers added by optimizer will multiply this size by 2. So, for training you consume ~5 GB just for output layer.
I suggest you to revise your network and try to reduce batch size / parameter counts to fit your model to GPU