MPEG-DASH Reference Software and Conformance
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Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient.
However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience.
In this paper, we introduce CAdViSE, our Cloud-based Adaptive
Video Streaming Evaluation framework for the automated testing
of adaptive media players. The paper aims to demonstrate a test
environment which can be instantiated in a cloud infrastructure,
examines multiple media players with different network attributes
at defined points of the experiment time, and finally concludes the
evaluation with visualized statistics and insights into the results.CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...Alpen-Adria-Universität
Recommended
Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient.
However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience.
In this paper, we introduce CAdViSE, our Cloud-based Adaptive
Video Streaming Evaluation framework for the automated testing
of adaptive media players. The paper aims to demonstrate a test
environment which can be instantiated in a cloud infrastructure,
examines multiple media players with different network attributes
at defined points of the experiment time, and finally concludes the
evaluation with visualized statistics and insights into the results.CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...Alpen-Adria-Universität
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In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70%, a reduction in the total number of CPU threads used by 63.83%, and a 37.87% reduction in the overall encoding time, considering a JND of six VMAF points.Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
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Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
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MPEG-DASH Reference Software and Conformance
- 1. WE MAKE YOUR VIDEOS FLOW Industry Leading Streaming Solutions DASH Reference Software and Conformance 2014 NAB Show DASH Talks NAB 14 April 8, Las Vegas, NV, USA Dipl.-Ing. Dr. Christian Timmerer CIO | bitmovin GmbH & Alpen-Adria-Universität Klagenfurt christian.timmerer@bitmovin.com blog.timmerer.com
- 2. Scope of Reference Software and Conformance • Conformance and reference software of MPEG-DASH serves three main purposes: – validation of the written specification; – clarification of the written specification; and – conformance testing for checking interoperability for the various applications against the reference software which aims to be compliant with ISO/IEC 23009 • ISO/IEC 23009-2: media presentation conformance, test vectors, DASH access engine reference software, sample software Scope 1
- 3. Components • Media presentation conformance – MPD validator: xlink, schema validation, add’l validation rules – Segment conformance: check ISOBMFF, M2TS against DASH specification – Dynamic service validator: dynamic updates of the MPD • DASH conformance sequences • DASH access client reference software – libdash: access to information contained in the MPD + schedule download of segments • Sample software – Sample player: utilizes libdash, GUI, manual adaptation logic – GPAC player: integrated player or embeddable DASH engine – MP4Box segmenter: create segments + MPD based on encoded content • Computing Now theme – Recent MPEG standards for future media ecosystems – http://bit.ly/1h5tMgq http://dashif.org/software/ Components 2
- 4. Cloud-based MPEG-DASH Transcoding and Streaming for Broadcast Scenarios • Using scalable & flexible cloud infrastructure • MPEG-DASH representations for mobile devices (320p) up to PC/TV (1080p) • MPEG-DASH playback on multiple devices using HTML5 & Flash clients • Live / Timeshift / OnDemand using the same MPEG- DASH content CLOUD-BASED TRANSCODING & STREAMING MPEG-DASH CLIENTS CUSTOMER PORTAL SHOWCASE Live Timeshift Catchup using EPG Data Cloud-DASH 3
- 5. Conclusions • MPEG-DASH Reference Software and Conformance – Comprehensive toolset for conformance testing – Verification and clarification of the written specification • Complementary DASH-IF software tools – Reference client, sample players, sample segmenters and packagers, libraries, manifest and ISOBMFF validator ➪ http://dashif.org/software/ Conclusions 4
- 6. WE MAKE YOUR VIDEOS FLOW Industry Leading Streaming Solutions Bitmovin We make your media flow Bitdash/Libdash bitmovin GmbH Lakeside Science and TechnologyPark Building B01 9020 Klagenfurt Austria | Europe T +43 463 27008747 E office@bitmovin.net www.bitmovin.com| www.bitcodin.com @bitmovin facebook.com/bitmovingithub.com/bitmovin