The decoding of context-based adaptive binary arithmetic coding (CABAC) imposes a heavy performance requirement on H.264/AVC decoding systems particularly for largescale video sequences. As a simple approach of elevating the operating frequency is not sufficient to meet the performance requirement, this paper proposes an efficient approach to accelerate the decoding, which is effective under relatively low operating frequency. Since the CABAC decoding procedure is highly sequential and has strong data dependencies, it is difficult to exploit parallelism and pipeline schemes. The proposed approach resolves the difficulties by modifying the operation chain based on a thorough analysis, eventually enabling both parallel operations and pipelining. More specifically, 1) several context models are simultaneously loaded from memory while context selection is performed in parallel; and 2) bin-level pipelining is enabled by employing a small storage to remove structural hazards and data dependencies. Experimental results show that the proposed approach leads to the decoding throughput of about 1 symbol/cycle, thus enabling the real-time decoding of HD sequences.