The MPEG Extensible Middleware API

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The document discusses the MXM API, which provides a simplified interface for accessing MPEG technologies through wrappers and libraries. The MXM API exposes key functionality through various engine classes, standardizing access to features across MPEG-4, MPEG-7, MPEG-21 and other standards. This reduces 11,000+ pages of MPEG specifications down to a 37 page API specification with around 500 standardized methods. The API aims to simplify integration of MPEG technologies while maintaining control at the upper levels and offering sufficient access points.

MXM API

Marius PREDA
Institut TELECOM / ARTEMIS

1st International MPEG Extensible Middleware
Developers Day (MXMDevDay) 2009
30 June, 2009, Queen Mary University London, UK

Definition
 In computer science, an Application
Programming Interface (API) is an interface
defining the ways by which an application
program may request services from the libraries
and/or operating systems which provide the
services.
 In MXM, services are MPEG functionalities and
libraries are wrappers on top of encoders,
decoders, parsers, …

media Old, yet good MPEG time …

video codec

audio codec
video

audio

compression functionalities

media Nowadays, life is more complex

graphics
primitives

scene
graph

video

audio

s ion n n n
es ip tio tio tio
pr cr te
c ta functionalities
m es o ap
co d pr ad

Motivation of providing API
 MPEG specifications: huge amount of technology
 157 standards in ISO/IEC 14496 (MPEG-4) family
 43 standards in ISO/IEC 15938 (MPEG-7) family
 33 standards in ISO/IEC 21000 (MPEG-21) family
 …

 Around 11 000 pages, 1,5 m high when printed

 However, majority of people developing MPEG related
applications do not need to know that is inside the boxes, but
only how to use it

How MXM prepared the packets?
1. Creating wrapping libraries, called engines

2. Opening the engines at ESSENTIAL points only

3. Documenting the IN/OUT points in another …
MPEG standard: 23006-2 MXM API

- only 37 pages ,
- around 500 methods

What is currently accessible through
the APIs?
 MPEG-4 :
 MediaFrameworkEngine
 MPEG-7 :
 MetadataEngine Almost all the engines exposes
 MPEG-21 :
 Digital Item Engine, Digital Item Streaming Engine, Creation and Access methods
 Digital Item Adaptation Engine,
 MPEG-21 File Engine,
 REL Engine,
 IPMP Engine,
 Event Reporting Engine,
 Content Protocol Engine,
 License Protocol Engine,
 IPMP Tool Protocol Engine,
 Content Search Engine,
 Security Engine,
 MVCO Engine, Domain Engine,
 Rendering Engine
 Orchestrator :
 Digital Item Adaptation Orchestrator Engine
 IPMP Orchestrator Engine
 REL Orchestrator Engine
 Player Orchestrator Engine

MediaFrameworkEngine API
Class Type Creation Decoding
Elementary Image Y Y
Streams
Audio Y (light) Y

Video Traditional Y (light) Y
(MPEG-1,
MPEG-2, etc.)

RVC

Muxed content Y Y

Graphics- Scene 2D/layout
related

3D/scene Y Y
graph
Graphic 2D
primitives Graphics

3D Y Y
Graphics

MediaFrameworkEngine API

Organized per functionality and per media

MetadataEngine API

Organized in three packages

Digital Item Adaptation Orchestrator
Engine
 Specifies means to adapt a Digital Item or media resource based
on
 the description of the content – ContentMetadataEngine;
 according to a given context – DIAEngine;
 utilizing an actual adaptation engine – MediaFrameworkEngine.

Conclusions
 MXM API benefits
 Simplifies the integration of MPEG technologies in third-parties
applications
 Exposes a balanced level of granularity to maintain the control at
upper level while offering enough access points
 It is documented in a developer friendly manner (hierarchical
structure of packages, interfaces, classes, methods)

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In today’s dynamic streaming landscape, where viewers access content on various devices and en- counter fluctuating network conditions, optimizing video delivery for each unique scenario is impera- tive. Video content complexity analysis, content-adaptive video coding, and multi-encoding methods are fundamental for the success of adaptive video streaming, as they serve crucial roles in delivering high-quality video experiences to a diverse audience. Video content complexity analysis allows us to comprehend the video content’s intricacies, such as motion, texture, and detail, providing valuable insights to enhance encoding decisions. By understanding the content’s characteristics, we can efficiently allocate bandwidth and encoding resources, thereby improving compression efficiency without compromising quality. Content-adaptive video coding techniques built upon this analysis involve dy- namically adjusting encoding parameters based on the content complexity. This adaptability ensures that the video stream remains visually appealing and artifacts are minimized, even under challenging network conditions. Multi-encoding methods further bolster adaptive streaming by offering faster encoding of multiple representations of the same video at different bitrates. This versatility reduces computational overhead and enables efficient resource allocation on the server side. Collectively, these technologies empower adaptive video streaming to deliver optimal visual quality and uninter- rupted viewing experiences, catering to viewers’ diverse needs and preferences across a wide range of devices and network conditions. Embracing video content complexity analysis, content-adaptive video coding, and multi-encoding methods is essential to meet modern video streaming platforms’ evolving demands and create immersive experiences that captivate and engage audiences. In this light, this dissertation proposes contributions categorized into four classes:

Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...

Alpen-Adria-Universität

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 Vid...

Alpen-Adria-Universität

Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...

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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

The document discusses research on using multi-access edge computing (MEC) to improve adaptive video streaming. It presents several contributions, including developing a MEC and HAS simulator called ANGELA, proposing dynamic segment repackaging at the edge to increase bandwidth savings, and designing edge-assisted adaptation schemes (EADAS and ECAS-ML) that leverage edge resources to guide client-based ABR algorithms and improve QoE and fairness. It also investigates segment prefetching policies at the edge such as ones based on last quality, Markov models, transrating, machine learning, and super-resolution.

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

This document discusses energy consumption in video streaming. It identifies the key components that consume energy, including encoding, storage, networks, edge components, decoding, and displays. Measurement tools and challenges are also covered. Strategies to reduce energy usage at each component are proposed, such as efficient encoding, optimized bitrates, CDN optimization, and energy-aware networking. A holistic, end-to-end approach is needed to minimize total energy consumption in video streaming.

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.

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The MPEG Extensible Middleware API

1. MXM API Marius PREDA Institut TELECOM / ARTEMIS 1st International MPEG Extensible Middleware Developers Day (MXMDevDay) 2009 30 June, 2009, Queen Mary University London, UK

2. Definition  In computer science, an Application Programming Interface (API) is an interface defining the ways by which an application program may request services from the libraries and/or operating systems which provide the services.  In MXM, services are MPEG functionalities and libraries are wrappers on top of encoders, decoders, parsers, …

3. media Old, yet good MPEG time … video codec audio codec video audio compression functionalities

4. media Nowadays, life is more complex graphics primitives scene graph video audio s ion n n n es ip tio tio tio pr cr te c ta functionalities m es o ap co d pr ad

5. Motivation of providing API  MPEG specifications: huge amount of technology  157 standards in ISO/IEC 14496 (MPEG-4) family  43 standards in ISO/IEC 15938 (MPEG-7) family  33 standards in ISO/IEC 21000 (MPEG-21) family  …  Around 11 000 pages, 1,5 m high when printed  However, majority of people developing MPEG related applications do not need to know that is inside the boxes, but only how to use it

6. How MXM prepared the packets? 1. Creating wrapping libraries, called engines 2. Opening the engines at ESSENTIAL points only 3. Documenting the IN/OUT points in another … MPEG standard: 23006-2 MXM API - only 37 pages , - around 500 methods

7. What is currently accessible through the APIs?  MPEG-4 :  MediaFrameworkEngine  MPEG-7 :  MetadataEngine Almost all the engines exposes  MPEG-21 :  Digital Item Engine, Digital Item Streaming Engine, Creation and Access methods  Digital Item Adaptation Engine,  MPEG-21 File Engine,  REL Engine,  IPMP Engine,  Event Reporting Engine,  Content Protocol Engine,  License Protocol Engine,  IPMP Tool Protocol Engine,  Content Search Engine,  Security Engine,  MVCO Engine, Domain Engine,  Rendering Engine  Orchestrator :  Digital Item Adaptation Orchestrator Engine  IPMP Orchestrator Engine  REL Orchestrator Engine  Player Orchestrator Engine

8. What is currently accessible through the APIs?  MPEG-4 :  MediaFrameworkEngine  MPEG-7 :  MetadataEngine Almost all the engines exposes  MPEG-21 :  Digital Item Engine, Digital Item Streaming Engine, Creation and Access methods  Digital Item Adaptation Engine,  MPEG-21 File Engine,  REL Engine,  IPMP Engine,  Event Reporting Engine,  Content Protocol Engine,  License Protocol Engine,  IPMP Tool Protocol Engine,  Content Search Engine,  Security Engine,  MVCO Engine, Domain Engine,  Rendering Engine  Orchestrator :  Digital Item Adaptation Orchestrator Engine  IPMP Orchestrator Engine  REL Orchestrator Engine  Player Orchestrator Engine

9. MediaFrameworkEngine API Class Type Creation Decoding Elementary Image Y Y Streams Audio Y (light) Y Video Traditional Y (light) Y (MPEG-1, MPEG-2, etc.) RVC Muxed content Y Y Graphics- Scene 2D/layout related 3D/scene Y Y graph Graphic 2D primitives Graphics 3D Y Y Graphics

10. MediaFrameworkEngine API Organized per functionality and per media

11. MediaFrameworkEngine API

12. MediaFrameworkEngine API

13. MediaFrameworkEngine API

14. MediaFrameworkEngine API

15. MediaFrameworkEngine API

16. MediaFrameworkEngine API

17. MediaFrameworkEngine API

18. MediaFrameworkEngine API

19. MediaFrameworkEngine API

20. MediaFrameworkEngine API

21. MediaFrameworkEngine API

22. MetadataEngine API Organized in three packages

23. MetadataEngine API

24. MetadataEngine API

25. MPEG-21FileEngine API

26. Digital Item Adaptation Orchestrator Engine  Specifies means to adapt a Digital Item or media resource based on  the description of the content – ContentMetadataEngine;  according to a given context – DIAEngine;  utilizing an actual adaptation engine – MediaFrameworkEngine.

27. Conclusions  MXM API benefits  Simplifies the integration of MPEG technologies in third-parties applications  Exposes a balanced level of granularity to maintain the control at upper level while offering enough access points  It is documented in a developer friendly manner (hierarchical structure of packages, interfaces, classes, methods)

The MPEG Extensible Middleware API

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The MPEG Extensible Middleware API