Spectrum sensing algorithms are developed for cognitive radios to support real-time traffic. Multiple bands are sensed in parallel to reduce sensing delay, while meeting a minimum rate requirement with a prescribed outage probability. Interference constraints are also imposed to protect primary user (PU) transmissions. Both fixed sample size (FSS) and sequential sensing algorithms are developed. In the FSS sensing, a series of convex feasibility problems are solved to minimize sensing delay. In the sequential case, a bank of sequential probability ratio tests (SPRTs) is employed to detect PU presence, where the detector parameters are optimized via convex optimization. Numerical tests demonstrate that the (average) sensing delay of sequential sensing is considerably smaller than that of FSS sensing.