Wireless systems with multiple antennas at both the transmitter and receiver (MIMO systems) have been the focus of research in the recent past due to their ability to provide higher data rates and better reliability than their single antenna counterparts. Designing a communication system for MIMO frequency selective channels provides many signal processing challenges. Popular methods like MIMOOFDM and space-time precoding linearly process blocks of data at both the transmitter and the receiver. Independence between the blocks is ensured by introducing sufficient redundancy between successive blocks. This approach has many pitfalls, including the limit on achievable data rate due to redundancy requirements and the need for additional coding/processing. In this thesis, we provide a filterbank precoding framework (FBP) for communication over MIMO frequency selective channels. By viewing the channel as a polynomial matrix, we derive the minimum redundancy required for achieving FIR equalization of the precoded channel. It is shown that, for most practical channels, a nominal redundancy is enough. The results are general, and hold for channels of any dimension and order. We derive the zero-forcing and MMSE equalizers for the precoded channel. The role of equalizer delay in system performance is analyzed. We extend the minimum redundancy result to the case of space-time filterbank precoding (STFP). Introducing the time dimension allows the channel to be represented by a block pseudocirculant matrix. By using the Smith form of block pseudocirculant matrices, we show that very high data rates can be achieved with STFP. When channel information is available at the transmitter, we derive an iterative algorithm for obtaining the MMSE optimal precoder-equalizer pair. We then provide a comparison of FBP with the block processing methods. It is shown that FBP provides better BER performance than the block processing methods at a lower computational cost. The reasons for the better performance of FBP are discussed.