The recent video compression standard, HEVC (high efficiency video coding), will most likely be used in various applications in the near future. However, the encoding process is far too slow for real-time applications. At the same time, computing capabilities of GPUs (graphics processing units) have become more powerful in these days. In this paper, we have proposed a GPU-based parallel motion estimation (ME) algorithm to enhance the performance of an HEVC encoder. A frame is partitioned into two subframes for pipelined execution to improve GPU utilization. The flow chart is redetermined to solve data hazards in the pipelined execution. Two new methods are introduced in the proposed ME: decision of a representative search center position (RSCP) and warp-based concurrent parallel reduction (WCPR). A RSCP employs motion vectors of a co-located CTU in a previously encoded frame to solve a dependency problem in parallel computation with negligible coding loss. WCPR concurrently executes several parallel reduction operations, which increases the thread utilization from 20 to 89 % without any thread synchronization. The proposed encoder can make the portion of ME in the encoder negligible with 2.2 % bitrate increase against the HEVC test model (HM) encoder. In terms of ME, the proposed ME is 130.7 times faster than that of the HM encoder.

A femtocell is a small cellular base station, typically designed for use in a home or small business. The random deployment of a femtocell has a critical effect on the performance of a macrocell network due to co‐channel interference. Utilizing the advantage of a multiple‐input multiple‐output system, each femto base station (FBS) is able to form a cluster and generates a precoding matrix, which is a modified version of conventional single‐cell block diagonalization, in a cooperative manner. Since interference from clustered‐FBSs located at the nearby macro user equipment (MUE) is the dominant interference contributor to the coexisting networks, each cluster generates a precoding matrix considering the effects of interference on nearby MUEs. Through simulation, we verify that the proposed algorithm shows better performance respective to both MUE and femto user equipment, in terms of capacity.

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In this letter, we propose a novel algorithm to predict MPEG‐coded real‐time variable bit rate (VBR) video traffic. From the frame size measurement, the algorithm extracts the statistical property of video traffic and utilizes it for the prediction of the next frame for I‐, P‐, and B‐ frames. The simulation results conducted with real‐world MPEG‐4 VBR video traces show that the proposed algorithm is capable of providing more accurate prediction than those in the research literature.

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