In this letter, we propose a Hadoop-based Video Transcoding System (HVTS), which is designed to run on all major cloud computing services. HVTS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To evaluate, our proposed system, we carry out two performance tests on our local testbed, transcoding and robustness to data node and task failures. The results confirmed that our system delivers satisfactory performance in facilitating seamless streaming services in cloud computing environments.

With the proliferation of hand-held devices in recent years, mobile video streaming has become an extremely popular application. However, Internet video streaming to mobile devices faces several problems, such as unstable connections, long latency, high jitter, etc. We present a system, OptVid, which enhances the user's experiences of video streaming service on cellular networks. OptVid takes the user's profile and provides seamless adaptive bitrate streaming by leveraging the video transcoding solution. It provides very agile bitrate adaptation, especially in the mobile scenario where the wireless channel is not stable. We prototype video transcoding on a WiMAX testbed to bridge the gap between the wireless channel capacity and the video quality. Our evaluations reveal that OptVid provides better user experience than conventional schemes in terms of PSNR, video stalls, and buffering time. OptVid does not require any additional storage since it transcodes videos on-the-fly upon receiving requests and delivers them directly to the client.

A direct inter-mode selection algorithm for P-frames in fast homogeneous H.264/AVC bit-rate reduction transcoding is proposed in this paper. To achieve the direct inter-mode selection, we firstly develop a low-complexity distortion estimation method for fast transcoding, in which the distortion is directly calculated from the decoded residual together with the reference frames. We also present a linear estimation method to approximate the coding rate. With the estimated distortion and rate, the rate-distortion cost can be easily computed in the transcoder. In our algorithm, a method based on the normalized rate difference of P-frames (RP) is used to detect the high motion scene. To achieve fast transcoding, only for the P-frames with RP larger than a threshold, the rate-distortion optimized (RDO) mode decision is performed; meanwhile, the average cost of each inter-mode (ACM) is calculated. Then for the subsequent frames transcoding, the optimal coding mode can be directly selected using the estimated cost and the ACM threshold. Experiments show that the proposed method can significantly simplify the complex RDO mode decision, and achieve transcoding time reductions of up to 62% with small loss of rate-distortion performance.

The coexistence of MPEG-2 and its powerful successor H.264/AVC has created a huge need for MPEG-2/H.264 video transcoding. However, a traditional transcoder where an MPEG-2 decoder is simply cascaded to an H.264 encoder requires huge computational power due to the adoption of a complicated rate-distortion based mode decision process in H.264. This paper proposes a 2-D Sobel filter based motion vector domain method and a DCT domain method to measure macroblock complexity and realize content-based H.264 candidate mode decision. A new local edge based fast INTRA prediction mode decision method is also adopted to boost the encoding efficiency. Simulation results confirm that with the proposed methods the computational burden of a traditional transcoder can be reduced by 20%30% with only a negligible bit-rate increase for a wide range of video sequences.

When the frame size is downscaled for video transcoding, the new motion vector (MV) must be computed. This paper presents an algorithm to utilize the activity measurement by DC value and the number of non-zero quantized DCT coefficients in the residual macroblock to compose the motion vector. It can reduce the complexity for motion estimation and improve the performance of the spatial domain video transcoder.

A novel motion vector re-estimation technique for transcoding into lower spatial resolution is proposed. This technique is based on the fact that the block matching error is proportional to the complexity of the reference block with Taylor series expansion. It is shown that the motion vectors re-estimated by the proposed method are closer to optimal ones and offer better quality than those of previous techniques.