This paper focuses on designing and analyzing throughput-optimal scheduling policies that avoid using per-flow or per-destination information, maintain a single data queue for each link, exploit only local information, and potentially improve the delay performance, for multi-hop wireless networks under general interference constraints. Although the celebrated backpressure algorithm maximizes throughput, it requires per-flow or per-destination information (which may be difficult to obtain and maintain), maintains per-flow or per-destination queues at each node, relies on constant exchange of queue length information among neighboring nodes to calculate link weights, and may result in poor delay performance. In contrast, the proposed schemes can circumvent these drawbacks while guaranteeing throughput optimality. We rigorously analyze the throughput performance of the proposed schemes and show that they are throughput-optimal using fluid limit techniques via an inductive argument. We also conduct simulations to show that the proposed schemes can substantially improve the delay performance.