In this paper, we aim to bridge the gap between the energy efficiency of software and hardware video decoders by combining both DVFS and parallelism. For this purpose, we, first, propose an adaptive DVFS algorithm for energy efficient mono-core decoding of H.264 videos. The proposed solution uses metadata (normalized by MPEG) providing information about the upcoming workload. These metadata are processed within an adaptive filter to build dynamically an accurate complexity model used to calculate the minimal processor frequencies for decoding video frames while guaranteeing real time constraints. Then, we generalize the proposed DVFS to slice-based multi-threaded parallel video decoders on multi-core platforms. Our performance evaluations showed that the proposed algorithm for mono-core decoding is able to converge to an accurate complexity model (4 percent) in less than 1 second. Moreover, it is simple to implement, induces very low overhead and achieves up to 46 percent energy saving as compared to the ondemand Linux DVFS governor. On the other hand, joint use of parallelism and DVFS allows 720p software video decoding with only 17 percent more energy consumption as compared to a hardware video decoder.

In this paper, we propose a network coding solution to improve transmission reliability of scalable video coding (SVC) streams over lossy networks. Since scalable video coding produces various levels of quality with variable bitrates, we design a prioritized network coding scheme combining different encoding approaches: generation-based and sliding window based schemes. We carried out a performance evaluation study of our scheme based on video (i.e., PSNR) and network (i.e., packet loss rate and overhead) metrics for two different test video sequences with quality scalability.

In this paper, we address the tradeoff between network coding overhead and multi-layer video tolerated loss rate on multi-hop wireless networks. We aim, through a simulation study, to evaluate a bounded network coding redundancy, for scalable video, based on the measured packet loss and the targeted maximum application loss rate. We consider first applying the bounded redundancy on a generation based scheme, and evaluate the tradeoff between tolerated video loss and the introduced overhead. Second, since scalable video produces a high video quality with various levels of importance, we propose to apply the bounded redundancy to an Expanding Window based scheme. We carried out a performance evaluation of both network coding schemes with two video test sequences using ns-3 simulator. We show the benefits of our scheme particularly in terms of overhead and PSNR.

In this paper, we present a simulation study of network coding schemes for multi-layer video streaming on multi-hop wireless networks. We consider in this study mainly two points: the packet loss due to the wireless environment and the unequal importance of scalable video packets. First, we study generation based network coding schemes with static and adaptive redundancy to evaluate the effect of redundancy adaptation on packet loss rate. Second, we evaluate an extension of the Expanding Window concept with a hop-by-hop coding to highlight the effect of layer discrimination for scalable video streaming. Extensive simulations have been carried out using ns- 3 for two different video test sequences with quality scalability. The simulation results show that adaptive redundancy can decrease the packet loss rate and introduce less overhead while Expanding Window can improve the PSNR of the video by reducing the base layer's packet loss.

This paper proposes a low computational method to perform ROI (Region Of Interest) based video encoding and adaptive streaming for remote rendering applications. The main objective of the proposed solution is to minimize the latency in the interactive loop even when facing poor transmission conditions. In order to do that, the knowledge of the depth map information provided by the rendering engine is exploited by the real-time video encoder to adapt the bitrate of the transmitted stream. Especially, thanks to an efficient coupling between the rendering and the video encoding stages, the macroblocks of each video frame are encoded with different quantization steps that follow an ROI partitioning. The details of this partitioning algorithm are provided as well with some implementation considerations. The simulation results demonstrate the benefit of our adaptive approach from the user experience point of view.

This paper proposes a video streaming system optimizing resource utilization when the media server only disposes of long term feedbacks from the client. Based on a partial knowledge of the network, we developed a scheduling algorithm that exploits the scalable video coding (SVC) properties to estimate packets importance and that takes into account packet delay dependencies to better anticipate congestion situations. Compared to more conventional streaming systems, experimental results show that our approach allows to better face network condition degradation like bandwidth reduction or packet error rate increase.

Transform image coding and more particularly the subclass of block based transformations are widely used to compress images. The JPEG standard for still images and MPEG codec specifications for video are very efficient implementations, but these algorithms perform image reconstruction without taking into account the quantization operations performed on the transform coefficients. In this paper, we propose an adaptive algorithm to tune the inverse transformation matrix as a function of the quantization level in order to minimize the reconstruction error. The developed algorithm provides quality scalability features and also integrates resizing operations into the inverse transformation process leading to a spatial scalability of fractional factors.