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In this new era of big data and Internet of Things (IoT), mobile traffic is expected to increase dramatically - a seven fold increase from 2016 to 2021. A key challenge for wireless communication systems is to provide high throughput, low delay and low age of information. By leveraging the high spectral efficiency, multi-channel and multi-antenna techniques have been considered as two important solutions to confront this performance challenge. Latest multi-channel and multi-antenna techniques such as OFDM, Full-duplex and FlexRadio have greatly changed the network in a variety of ways, and existing scheduling policies either do not work or only achieve sub-optimal performance in this new environment. This fact motivates us to investigate the fundamental problem of designing high performance scheduling policies in multi-channel and multi-antenna networks. We first study the delay minimization problem in a single-cell multi-channel (e.g., OFDM-based) cellular network. Compared to existing works, we consider a more practical setting with time-correlated channels. We show that the class of oldest packets first (OPF) policies that give a higher priority to packets with a large delay is delay rate-function optimal under two conditions: 1) The channel is non-negatively correlated, and 2) The distribution of the OFF period is geometric.Next, we shift our focus to a cellular system with Full-duplex MIMO Base Stations. An essential step for achieving multiplexing gain in MIMO downlink systems is to collect accurate channel state information (CSI) from the users. In this dissertation, we propose a novel approach to mitigate the feedback overhead by leveraging Full-duplex radios to enable concurrent channel probing and data transmission. We develop a throughput optimal scheduling policy with complexity O((N/I)^I), where N is the number of users and I is the number of user groups. To further reduce the complexity, we propose a greedy policy with complexity O(N log N) that not only achieves at least 2/3 of the optimal throughput region but also outperforms any feasible Half-duplex solutions. FlexRadio is another recent breakthrough in wireless Multi-RF technology that has introduced a new way to unify MIMO and full-duplex into a single framework with a fully flexible design. FlexRadio allows a wireless node to use an arbitrary number of RF chains to support transmission and reception, which makes MIMO and full-duplex subset configurations of FlexRadio. In this dissertation, we investigate the throughput maximization problem exploiting multi-antenna resources in FlexRadio networks. We first formulate the joint antenna allocation and link scheduling problem with RF resource constraints. A CSMA-like framework is then proposed to fully leverage the flexibility offered by FlexRadio. In addition, we use numerical simulations to validate the theoretical results and compare throughput under different configurations.Finally, we focus on the age minimization problem in a time-sensitive information update system with time-varying multi-channel resources. We first derive a policy-independent lower bound based on both inter-arrival and inter-service times. Two classes of matching policies are proposed to achieve constant competitive ratio compared to the optimal objective value. With the help from multi-channel multiplexing, we show that even some simple age-blind policies provide good age performance in the asymptotic regime, which marks a fundamental difference between optimizing age and delay.