MPEG-DASH Reference Software and Conformance

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The document discusses MPEG-DASH reference software and conformance. It describes how the reference software serves to validate the specification, clarify ambiguities, and test for interoperability. Components include an MPD validator, segment conformance checker, dynamic service validator, DASH access client software, and sample players and encoding tools. The reference software provides a comprehensive toolset for conformance testing and verification of the DASH specification.

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

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

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

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

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

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

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HTTP Adaptive Streaming (HAS) methods divide a video into smaller segments, encoded at multiple pre-defined bitrates to construct a bitrate ladder. Bitrate ladders are usually optimized per title over several dimensions, such as bitrate, resolution, and framerate. This paper adds a new dimension to the bitrate ladder by considering the energy consumption of the encoding process. Video encoders often have multiple pre-defined presets to balance the trade-off between encoding time, energy consumption, and compression efficiency. Faster presets disable certain coding tools defined by the codec to reduce the encoding time at the cost of reduced compression efficiency. Firstly, this paper evaluates the energy consumption and compression efficiency of different x265 presets for 500 video sequences. Secondly, optimized presets are selected for various representations in a bitrate ladder based on the results to guarantee a minimal drop in video quality while saving energy. Finally, a new per-title model, which optimizes the trade-off between compression efficiency and energy consumption, is proposed. The experimental results show that decreasing the VMAF score by 0.15 and 0.39 while choosing an optimized preset results in encoding energy savings of 70% and 83%, respectively.

Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...

Alpen-Adria-Universität

With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission. To tackle this issue, we introduce an online energy-efficient Multi-Codec Bitrate ladder Estimation scheme (MCBE) for adaptive video streaming applications. In MCBE, quality representations within the bitrate ladder of new-generation codecs (e.g., HEVC, AV1) that lie below the predicted rate-distortion curve of the AVC codec are removed. Moreover, perceptual redundancy between representations of the bitrate ladders of the considered codecs is also minimized based on a Just Noticeable Difference (JND) threshold. Therefore, random forest-based models predict the VMAF of bitrate ladder representations of each codec. In a live streaming session where all clients support the decoding of AVC, HEVC, and AV1, MCBE achieves impressive results, reducing cumulative encoding energy by 56.45%, storage energy usage by 94.99%, and transmission energy usage by 77.61% (considering a JND of six VMAF points). These energy reductions are in comparison to a baseline bitrate ladder encoding based on current industry practice.

Machine Learning Based Resource Utilization Prediction in the Computing Conti...

Alpen-Adria-Universität

This paper presents UtilML, a novel approach for tackling resource utilization prediction challenges in the computing continuum. UtilML leverages Long-Short-Term Memory (LSTM) neural networks, a machine learning technique, to forecast resource utilization accurately. The effectiveness of UtilML is demonstrated through its evaluation of data extracted from a real GPU cluster in a computing continuum infrastructure comprising more than 1800 computing devices. To assess the performance of UtilML, we compared it with two related approaches that utilize a Baseline-LSTM model. Furthermore, we analyzed the LSTM results against User-Predicted values provided by GPU cluster owners for task deployment with estimated allocation values. The results indicate that UtilML outperformed user predictions by 2% to 27% for CPU utilization prediction. For memory prediction, UtilML variants excelled, showing improvements of 17% to 20% compared to user predictions.

Evaluation of Quality of Experience of ABR Schemes in Gaming Stream

Alpen-Adria-Universität

The exponential growth of computer game streaming has led to the development of Quality of Experience (QoE) metrics to evaluate user satisfaction and enjoyment during online gameplay and live streaming. Adaptive Bitrate (ABR) streaming is a recent technology that has been suggested to improve QoE. This method enhances the streaming experience, upholds visual quality, minimizes stall events, and boosts player retention. It achieves this by estimating network bottlenecks and selecting appropriate versions of the content that best match the available bandwidth rather than adjusting encoding parameters. To investigate the correlation between quality switching and stall events, a subjective test was conducted separately and comparatively with 71 participants. For more detailed and in-depth research, video games were analyzed with the Video Complexity Analyzer (VCA) tool and divided into three categories of different genres, camera view, and temporal complexity heatmap from the two sets of normal and action scenes. This study seeks to shed light on three unresolved issues pertinent to QoE in game streaming: (i) the user preferences towards quality switching and stall events across varied scenes and games, (ii) the user inclinations towards either a single, prolonged stall event or multiple, shorter stall events, and (iii) the impact of conspicuous quality switching on the user’s QoE. Results from the study provided valuable insights, both qualitatively and quantitatively. The study found a marked preference among users for quality switching over stall events across all types of game streaming, irrespective of the scene’s intensity. Furthermore, it was observed that multiple short-stall events were generally favored over a single long-stall event in streaming first-person shooting games. Interestingly, approximately half of the participants remained oblivious to quality switching during their game viewing sessions, and among those who noticed a change in quality, the alteration did not significantly impact their perceived QoE.

Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...

Alpen-Adria-Universität

Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low- latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client- based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.

Multi-access Edge Computing for Adaptive Video Streaming

Alpen-Adria-Universität

Over the last recent years, video streaming traffic has become the dominating service over mobile networks. The two main reasons for the growth of video streaming traffic are the improved capabilities of mobile devices and the emergence of HTTP Adaptive Streaming (HAS). Hence, there is a demand for new technologies to cope with the increasing traffic load while improving clients’ Quality of Experience (QoE). The network plays a crucial role in the video streaming process. One of the key technologies on the network side is Multi-access Edge Computing (MEC), which has several key characteristics: computing power, storage, proximity to the clients and access to network and player metrics. Thus, it is possible to deploy mechanisms at the MEC node that assist video streaming. This thesis investigates how MEC capabilities can be leveraged to support video streaming delivery, specifically to improve the QoE, reduce latency or increase storage and bandwidth savings.

Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment

Alpen-Adria-Universität

In the last decades, video streaming has been developing significantly. Among current technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side.

VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...

Alpen-Adria-Universität

The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today’s internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.

Energy Consumption in Video Streaming: Components, Measurements, and Strategies

Alpen-Adria-Universität

The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to measure energy consumption for each component accurately and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming [1, 2, 3]. These components are classified into three categories [4]: (i) data centers, which include encoding, packaging, and storage on cloud data centers; (ii) networks, which include core network and access networks; and (iii) end-user devices which involve decoding, players, hardware, etc. In addition to identifying the primary components of video streaming that affect energy consumption, it is important to conduct a comprehensive analysis of the entire video streaming. It is also essential to balance energy optimization and service quality to ensure that energyefficient strategies are implemented without sacrificing the quality of video streaming services. This talk aims to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.

Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...

Alpen-Adria-Universität

The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices. In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.

Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning

Alpen-Adria-Universität

Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video. As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance. This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning.

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...

Alpen-Adria-Universität

Nowadays, HTTP Adaptive Streaming (HAS) has become the de-facto standard for delivering video over the Internet. More users have started generating and delivering high-quality live streams (usually 4K resolution) through popular online streaming platforms, resulting in a rise in live streaming traffic. Typically, the video contents are generated by streamers and watched by many audiences, geographically distributed in various locations far away from the streamers. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. This dissertation leverages edge computing capabilities and in-network intelligence to design, implement, and evaluate approaches to optimize Quality of Experience (QoE) and end-to-end (E2E) latency of live HAS. In addition, improving transcoding performance and optimizing the cost of running live HAS services and the network’s backhaul utilization are considered. Motivated by the mentioned issue, the dissertation proposes five contributions in two classes: optimizing resource utilization and light-weight transcoding.

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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

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