With the ever-growing demand for higher data rate in next generation communication systems, researchers are pushing the limits of the existing architecture. Due to the stochastic nature of communication channels, most systems use some form of adaptive methods to adjust the transmitting parameters and allocation of resources in order to overcome channel variations and achieve optimal throughput. We will study four cases of adaptive transmission and dynamic resource allocation in collaborative systems that are practically significant. Firstly, we study hybrid automatic repeat request (HARQ) techniques that are widely used to handle transmission failures. We propose HARQ policies that improve system throughput and are suitable for point-to-point, two-hop relay, and multi-user broadcast systems. Secondly, we study the effect of having bits of mixed SNR qualities in finite length codewords. We prove that by grouping bits according to their reliability so that each codeword contains homogeneous bit qualities, the finite blocklength capacity of the system is increased. Thirdly, we study the routing and resource allocation problem in multiple collaborative networks. We propose an algorithm that enables collaboration between networks which needs little to no side information shared across networks, but rather infers necessary information from the transmissions. The collaboration between networks provides a significant gain in overall throughput compared to selfish networks. Lastly, we present an algorithm that allocates disjoint transmission channels for our cognitive radio network in the DARPA Spectrum Collaboration Challenge (SC2). This algorithm uses the real-time spectrogram knowledge perceived by the radios and allocates channels adaptively in a crowded spectrum shared with other collaborative networks.