This thesis deals with event-triggered model predictive control (MPC) strategies for constrained networked and distributed control systems. A networked control system usually consists of spatially distributed sensors, actuators and controllers that communicate over a shared communication network. Event-triggered control approaches consider the network utilization in the controller design to provide a compromise between control performance and communication effort. In this thesis a holistic output-based MPC scheme for constrained linear systems with event-triggered communication over the sensor-to-controller and controller-to-actuator channels of a network is presented. The proposed approach can be applied to centralized as well as decentralized setups and handles bounded time-varying sampling intervals and transmission delays for the control of constrained sampled-data systems. In distributed control set-ups the overall plant is decomposed into subsystems which are controlled by local controllers. Different distributed model predictive control (DMPC) approaches with reduced communication effort are presented in this thesis. The first approach is non-iterative and uses event-triggered communication for the exchange of state measurements. In the second approach, an event-triggered cooperation strategy for DMPC based on distributed optimization is introduced. Finally, an economic DMPC scheme for linear periodically time-varying systems which is motivated by two real-world applications, the control of a water distribution network and a medium voltage power grid, is presented.