This thesis presents packet-level channel modeling, spectrum efficiency optimization and channel estimation for wireless cooperative communication systems with diversity combining. Cooperative transmission in a wireless network allows neighboring nodes to share their communication resources to create a virtual antenna array by distributed transmission and signal processing, which is useful to exploit spatial diversity, increase channel capacity, and attain wider service coverage with single-antenna terminals. How to exploit spatial diversity and leverage the multi-hop channel structure is an important research issue for the cooperative network. In this thesis, two cooperative schemes are considered, amplify and forward (AF) and demodulation and forward (DMF). For AF cooperative systems, finite state Markov chain (FSMC) models are designed in analyzing the system performance considering time-varying channel behaviors and facilitating fast channel simulation. For DMF cooperative systems, first we formulate the optimization problem that jointly chooses the modulation schemes at the source and relay nodes, to maximize the throughput of cooperative systems under the BER constraint. Second, we propose to use the soft values of each bit to devise a simple and effective combining scheme, which can be applied for both AF and DMF cooperative systems. Third, as the soft valuesfrom demodulation process can also be used for measuring the channel estimation accuracy, a soft value-assisted channel estimation has been proposed by iteratively utilizing soft values to refine the accurate channel estimation. In addition, we also implement the soft value module in OFDM-based transceiver system based on a GNU Radio/USRP2 platform, and verify the effectiveness and performance improvement for the proposed SVC systems. As considering wireless cooperative systems has attracted increasing attentions from both academic and industry to meet the demanding of the high data rate transmission, the packet-level channel modeling, adaptive modulation, spectrum efficiency improvement frameworks based on soft value combining and accurate channel estimationalgorithms proposed in this thesis are essential for future proliferation of high data rate, reliable and efficient wireless communication networks.