This paper presents a novel decoding-workload-aware video encoding scheme. It takes raw video data and decoding workload constraint of a mobile client as input and generates a video bitstream which matches such a constraint while striving to achieve the best video quality. For a given constraint, the best overall video quality of the encoded bitstream is selected with a tradeoff between spatial and temporal distortions. The main contributions of this paper include: 1) the proposal of an efficient scheme which selects the most suitable target frame rate before the actual encoding; 2) The design of a workload control (analogous to the rate control) scheme which ensures an accurate control of the decoding workload when the bitstream is generated using the proposed encoding scheme. Experimental results demonstrate the feasibility and performance of the proposed scheme.

Video applications on different mobile devices are becoming increasingly popular. It is an attractive alternative to transcode a high quality non-scalable video bitstream to match constraints (such as bandwidth or processing power) of different platforms with a similar functionality as a scalable video format. In principle, such a transcoder can reduce either the bit per frame (bpf) or the frame per second (fps) of the original bitstream to meet a particular constraint. In the case that multiple candidates with different combinations of bpf and fps satisfy the constraint, an objective video quality measure is needed for the transcoder to choose the candidate with the overall best quality considering both the spatial quality (reflected by bpf) and the temporal quality (reflected by fps). Conventional measures, such as PSNR and MSE operate in the pixel-domain, require full decoding of both the original and candidate video bitstreams and are computationally very expensive. This drawback renders them unsuitable for real-time transcoding applications. To solve this problem, we propose a Mean Compressed Domain Error (MCDE) to predict the quality of the transcoded video. Experimental results show that the proposed MCDE can predict video quality accurately with a negligible computational complexity in comparison with the conventional MSE/PSNR.

Multimedia playback is restricted by the processing power of mobile devices, and in particular, the playback quality can be degraded due to insufficient processing power. To address this problem, we propose a new workload-scalable transcoding scheme which converts a pre-recorded video bitstream into a new video bitstream that satisfies the device's workload constraint, while keeping the transcoding distortion minimal. The key of this proposed transcoding scheme lies on a new workload prediction model, which is fast, accurate and is generic enough to apply to different video formats, decoder implementations and target platforms. The main contributions of this paper include 1) a workload prediction model for decoding MPEG video based on an offline bitstream analysis method; 2) a transcoding scheme that uses the proposed model to control the decoding workload on the target device. To facilitate our transcoding scheme, we have proposed a compressed domain distortion measure (CDDM) that takes effects from both frames per second (fps) and bits per frame (bpf) into consideration. CDDM ensures the transcoded video bitstream to have the best playback quality given the device's workload constraint. Both the workload prediction model and the transcoding scheme are evaluated experimentally.