Adaptive Linear Prediction for Resource Estimation of Video Decoding

Current systems often assume "worst case" resource utilization for the design and implementation of compression techniques and standards, thereby neglecting the fact that multimedia coding algorithms require time-varying resources, which differ significantly from the "worst case" requirements. To enable adaptive resource management for multimedia systems, resource-estimation mechanisms are needed. Previous research demonstrated that online adaptive linear prediction techniques typically exhibit superior efficiency to other alternatives for resource prediction of multimedia systems. In this paper, we formulate the problem of adaptive linear prediction of video decoding resources by analytically expressing the possible adaptation parameters for a broad class of video decoders. The resources are measured in terms of the time required for a particular operation of each decoding unit (e.g., motion compensation or entropy decoding of a video frame). Unlike prior research that mainly focuses on estimation of execution time based on previous measurements (i.e., based on autoregressive prediction or platform and decoder-specific off-line training), we propose the use of generic complexity metrics (GCMs) as the input for the adaptive predictor. GCMs represent the number of times the basic building blocks are executed by the decoder and depend on the source characteristics, decoding bit rate, and the specific algorithm implementation. Different GCM granularities (e.g., per video frame or macroblock) are explored. Our previous research indicated that GCMs can be measured or modeled at the encoder or the video server side and they can be streamed to the decoder along with the compressed bitstream. A comparison of GCM-based versus autoregressive adaptive prediction over a large range of adaptation parameters is performed. Our results indicate that GCM-based prediction is significantly superior to the autoregressive approach and also requires less computational resources at the decoder. As a result, a novel resource-prediction tradeoff is explored between: 1) the communication overhead for GCMs and/or the implementation overhead for the realization of the predictor and 2) the improvement of the prediction performance. Since this tradeoff can be significant for the decoder platform (either from the communication or the implementation perspective), we propose complexity (or communication)-bounded adaptive linear prediction in order to derive the best resource estimation under the given implementation (or GCM-communication) bound