Low-Complexity Adaptive Streaming via Optimized A Priori Media Pruning

Abstract — Source pruning is performed whenever the data rate of the compressed source exceeds the available communication or storage resources. In this paper, we propose a framework for rate-distortion optimized pruning of a video source. The framework selects which packets, if any, from the compressed representation of the source should be discarded so that the data rate of the pruned source is adjusted accordingly, while the resulting reconstruction distortion is minimized. The framework relies on a rate-distortion preamble that is created at compression time for the video source and that comprises the video packets ’ sizes, interdependencies and distortion importances. As one application of the pruning framework, we design a low-complexity rate-distortion optimized ARQ scheme for video streaming. In the experiments, we examine the performance of the pruning framework depending on the employed distortion model that describes the effect of packet interdependencies on the reconstruction quality. In addition, our experimental results show that the enhanced ARQ technique provides significant performance gains over a conventional system for video streaming that does not take into account the different importance of the individual video packets. These gains are achieved without an increase in packet scheduling complexity, which makes the proposed technique suitable for online R-D optimized streaming. I

Adaptation en temps réel pour une meilleure qualité d'expérience en réalité augmentée

In the framework of mobile augmented reality, a video stream is sent to the user with the help of a wireless communication link. To guarantee an efficient transmission, the video stream rate is controlled by adapting the encoding parameters such as to follow a given bandwidth. The rate can be reduced by reducing the frame rate and/or by choosing a higher compression factor for the video stream. These parameter modifications impact both the level of detail and the fluidity perceived by the user, and thus his/her subjective appreciation. The experience perceived by the user also depends on the context. During a rapid head motion, the notion of fluidity is more important than for a fixed head position. We propose an end-to-end adaptation scheme which enables the encoding of parameters such as to provide the best experience for the user regarding the dynamical context. For example, when the user moves quickly his/her head, the frame is compressed more to increase the frame rate and hence achieve a better perception of the motion. The lack of direct measurement for the subjective user experience is addressed with the design of objective metrics and a generic model to predict the user quality of experience in real time. A rate control strategy based on a systems approach is deployed to manage the multiple encoding parameters which control the stream rate. The encoder is modeled in an abstract manner as a single-variable linear system, where the content variation is taken as a perturbation. A stable and efficient controller is designed for the abstract model of the encoder. To implement the designed controller, the parameter combinations for the real encoder corresponding to the single input of the abstract model should be determined. A new one-pass algorithm determines this correspondence in real time based on a mapping method. Then, the proposed contextual adaptation enables to get the encoding parameter combination that maximizes the quality of experience using an appropriate model. Finally, the global adaptation scheme combines the rate control, the mapping method and the contextual adaptation for real-time implementation. Simulation and experiments illustrate the approach and the global adaptation scheme is validated through different scenarios