Dynamic Adaptive Streaming over HTTP (DASH) is referred to as a multimedia streaming standard to deliver high quality multimedia content over the Internet using conventional HTTP Web servers. As a fundamental feature, it enables automatic switching of quality levels according to network conditions, user requirements, and expectations. Currently, the proposed adaptation schemes for HTTP streaming mostly rely on throughput measurements and/or buffer-related metrics, such as buffer exhaustion and levels. In this paper, we propose to enhance the DASH adaptation logic by feeding it with additional information from our evaluation of the users' perception approximating the userperceived quality of video playback. The proposed model aims at conveniently combining TCP-, buffer-, and media content-related metrics as well as user requirements and expectations to be used as an input for the DASH adaptation logic. Experiments have demonstrated that the chosen model enhances the capability of the adaptation logic to select the optimal video quality level. Finally, we integrated all our findings into a real DASH system with QoE monitoring capabilities.

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International audienceWith the emergence of the High Efficiency Video Coding (HEVC) standard, a dataflow description of the decoder part was developed as part of the MPEG-B standard. This dataflow description presented modest framerate results which led us to propose methodolo-gies to improve the performance. In this paper, we introduce architectural improvements by exposing more parallelism using YUV and frame-based parallel decoding. We also present platform optimizations based on the use of SIMD functions and cache efficient FIFOs. Results show an average acceleration factor of 5.8 in the decoding framerate over the reference architecture

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