Nowadays video is an important part of the Web and Web sites like YouTube, Hulu, etc. count millions of users consuming their content every day. However, these Web sites mainly use media players based on proprietary browser plug-ins (i.e., Adobe Flash) and do not leverage adaptive streaming systems. This paper presents a seamless integration of the recent MPEG standard on Dynamic Adaptive Streaming over HTTP (DASH) in the Web using the HTML5 video element. Therefore, we present DASH-JS, a JavaScript-based MPEG-DASH client which adopts the Media Source API of Google’s Chrome browser to present a flexible and potentially browser independent DASH client. Furthermore, we present the integration of WebM based media segments in DASH giving a detailed description of the used container format structure and a corresponding Media Presentation Description (MPD). Our preliminary evaluation demonstrates the bandwidth adaption capabilities to show the effectiveness of the system.
MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.
libdash is a library that provides an object orient (OO) interface to the MPEG-DASH standard.
Features
- Cross platform build system based on cmake that includes Windows, Linux and Mac.
- Open source available and licensed under the LGPL.
- Implements the full MPEG-DASH standard according to ISO/IEC 23009-1, Information Technology Dynamic Adaptive Streaming over HTTP (DASH) Part 1: Media Presentation Description and Segment Formats
- Handles the download and xml parsing of the MPD. Based on that it provides an OO based interface to the MPD.
Media elements, e.g., SegmentURL, SegmentTemplate, etc., are downloadable in that OO based structure and can be downloaded through libdash, which internally uses libcurl.
- Therefore basically all protocols that libcurl supports, e.g., HTTP, FTP, etc. are supported by libdash.
- However it also provides a configurable download interface, which enables the use of external connections that can be implemented by the user of the library for the download of media segments.
- The use of such external connections will be shown in the libdash_networkpart_test project which is part of libdash solution and also part of the cross platform cmake system and therefore usable on Windows, Linux and Mac.
- The project contains a sample multimedia player that is based on ffmpeg which uses libdash for the playback of one of our dataset MPDs.
- The development is based on Windows, therefore the code contains a VS10 solution with additional tests and the sample multimedia player.
MPEG Dynamic Adaptive Streaming over HTTP (DASH) is a new streaming standard that has been recently ratified as an international standard (IS). In comparison to other streaming systems, e.g., HTTP progressive download, DASH is able to handle varying bandwidth conditions providing smooth streaming. Furthermore, it enables NAT and Firewall traversal, flexible and scalable deployment as well as reduced infrastructure costs due to the reuse of existing Internet infrastructure components, e.g., proxies, caches, and Content Distribution Networks (CDN). Recently, the Hypertext Transfer Protocol Bis (httpbis) working group of the IETF has officially started the development of HTTP 2.0. Initially three major proposals have been submitted to the IETF i.e., Googles' SPDY, Microsofts' HTTP Speed+Mobility and Network-Friendly HTTP Upgrade, but SPDY has been chosen as working draft for HTTP 2.0. In this paper we implemented MPEG-DASH over HTTP 2.0 (i.e., SPDY), demonstrating its potential benefits and drawbacks. Moreover, several experimental evaluations have been performed that compare HTTP 2.0 with HTTP 1.1 and HTTP 1.0 in the context of DASH. In particular, the protocol overhead, the performance for different round trip times, and DASH with HTTP 2.0 in a lab test scenario has been evaluated in detail.
libdash is a library that provides an object orient (OO) interface to the MPEG-DASH standard.
Features
- Cross platform build system based on cmake that includes Windows, Linux and Mac.
- Open source available and licensed under the LGPL.
- Implements the full MPEG-DASH standard according to ISO/IEC 23009-1, Information Technology Dynamic Adaptive Streaming over HTTP (DASH) Part 1: Media Presentation Description and Segment Formats
- Handles the download and xml parsing of the MPD. Based on that it provides an OO based interface to the MPD.
Media elements, e.g., SegmentURL, SegmentTemplate, etc., are downloadable in that OO based structure and can be downloaded through libdash, which internally uses libcurl.
- Therefore basically all protocols that libcurl supports, e.g., HTTP, FTP, etc. are supported by libdash.
- However it also provides a configurable download interface, which enables the use of external connections that can be implemented by the user of the library for the download of media segments.
- The use of such external connections will be shown in the libdash_networkpart_test project which is part of libdash solution and also part of the cross platform cmake system and therefore usable on Windows, Linux and Mac.
- The project contains a sample multimedia player that is based on ffmpeg which uses libdash for the playback of one of our dataset MPDs.
- The development is based on Windows, therefore the code contains a VS10 solution with additional tests and the sample multimedia player.
Our presentation from the media web symposium 2013 in Berlin on the open source landscape around MPEG-DASH as well as on cloud-based services for MPEG-DASH
Dynamic Adaptive Streaming over HTTP (DASH) is a
convenient approach to transfer videos in an adaptive and
dynamic way to the user. As a consequence, this system
provides high bandwidth flexibility and is especially
suitable for mobile use cases where the bandwidth variations
are tremendous. In this paper we have integrated the
Scalable Video Coding (SVC) extensions of the Advanced
Video Coding (AVC) standard into the recently ratified
MPEG-DASH standard. Furthermore, we have evaluated
our solution under restricted conditions using bandwidth
traces from mobile environments and compared it with an
improved version of our MPEG-DASH implementation
using AVC as well as major industry solutions.
MPEG DASH – Tomorrow's Format Today by Nicolas Weil
Senior Solutions Architect, Akamai Technologies & Will Law, Chief Architect, Media Cloud Engineering, Akamai Technologies
As an open standard designed to help simplify video delivery across connected devices, MPEG-DASH is continuing to gain momentum in the OTT, broadcast and wireless industries. Join Akamai's DASH experts for a discussion on what differentiates the emerging standard from legacy formats along with a demonstration showing the ease of deploying DASH playback across devices. The panel will also highlight current deployments, offer a review of the industry and provide a three-year outlook.
Akamai Edge is the premier event for Internet innovators, tech professionals and online business pioneers who together are forging a Faster Forward World. At Edge, the architects, experts and implementers of the most innovative global online businesses gather face-to-face for an invaluable three days of sharing, learning and together pushing the limits of the Faster Forward World. Learn more at: http://www.akamai.com/edge
Real-time entertainment services deployed over the open, unmanaged Internet – streaming audio and video – account now for more than 70% of the Internet traffic and it is assumed that this number will reach 80% by 2021. The technology used for such services is commonly referred to as HTTP Adaptive Streaming (HAS) and is widely adopted by various platforms such as YouTube, Netflix, Flimmit, etc. thanks to the standardization of MPEG-DASH and HLS. This talk will provide an overview of HAS, the state of the art of selected deployment options, and reviews work-in-progress as well challenges ahead. The main challenge can be characterized by the fact that (i) content complexity increases, (ii) delay or latency are vital application requirements, and (iii) Quality of Experience cannot be neglected anymore.
InterTech is a general contractor in MoscowMaxim Gavrik
InterTech is a general contractor in Moscow, which provides a full range of services for design, construction, installation, commissioning and start-up of the MEP systems in the buildings and structures under industrial, commercial and civil construction.
Our presentation from the media web symposium 2013 in Berlin on the open source landscape around MPEG-DASH as well as on cloud-based services for MPEG-DASH
Dynamic Adaptive Streaming over HTTP (DASH) is a
convenient approach to transfer videos in an adaptive and
dynamic way to the user. As a consequence, this system
provides high bandwidth flexibility and is especially
suitable for mobile use cases where the bandwidth variations
are tremendous. In this paper we have integrated the
Scalable Video Coding (SVC) extensions of the Advanced
Video Coding (AVC) standard into the recently ratified
MPEG-DASH standard. Furthermore, we have evaluated
our solution under restricted conditions using bandwidth
traces from mobile environments and compared it with an
improved version of our MPEG-DASH implementation
using AVC as well as major industry solutions.
MPEG DASH – Tomorrow's Format Today by Nicolas Weil
Senior Solutions Architect, Akamai Technologies & Will Law, Chief Architect, Media Cloud Engineering, Akamai Technologies
As an open standard designed to help simplify video delivery across connected devices, MPEG-DASH is continuing to gain momentum in the OTT, broadcast and wireless industries. Join Akamai's DASH experts for a discussion on what differentiates the emerging standard from legacy formats along with a demonstration showing the ease of deploying DASH playback across devices. The panel will also highlight current deployments, offer a review of the industry and provide a three-year outlook.
Akamai Edge is the premier event for Internet innovators, tech professionals and online business pioneers who together are forging a Faster Forward World. At Edge, the architects, experts and implementers of the most innovative global online businesses gather face-to-face for an invaluable three days of sharing, learning and together pushing the limits of the Faster Forward World. Learn more at: http://www.akamai.com/edge
Real-time entertainment services deployed over the open, unmanaged Internet – streaming audio and video – account now for more than 70% of the Internet traffic and it is assumed that this number will reach 80% by 2021. The technology used for such services is commonly referred to as HTTP Adaptive Streaming (HAS) and is widely adopted by various platforms such as YouTube, Netflix, Flimmit, etc. thanks to the standardization of MPEG-DASH and HLS. This talk will provide an overview of HAS, the state of the art of selected deployment options, and reviews work-in-progress as well challenges ahead. The main challenge can be characterized by the fact that (i) content complexity increases, (ii) delay or latency are vital application requirements, and (iii) Quality of Experience cannot be neglected anymore.
InterTech is a general contractor in MoscowMaxim Gavrik
InterTech is a general contractor in Moscow, which provides a full range of services for design, construction, installation, commissioning and start-up of the MEP systems in the buildings and structures under industrial, commercial and civil construction.
Mapa Mental del ecosistema y la Preservación del Medio Ambiente.Nathalie Cermeño
Este mapa mental fue realizado para dar a conocer y a entender un poco mas de este tema tan importante como lo es La Ecología, su definición con una imagen clara, sus niveles de estudio y como cuidarlo también mencionamos el tema de la la educación ambiental, el como concientizar para preservar nuestro medio ambiente para crear un mundo mejor, el que No debemos hacer para que nuestro medio ambiente se vea afectado, como rescatar nuestro medio ambiente, y como ayudar al mismo.
Al momento de recibir el evangelio de Jesucristo, hay muchas verdades que ocurren en mi vida y no las llego a conocer teniendo un desconocimiento y en muchas ocasiones subestimando la gracia de Dios en mí.
Jakie korzyści płyną z korzystania z płatności elektronicznych? Jak przebiega implementacja systemów płatności? Stripe, Braintree czy Venmo - jakie są wady i zalety każdego z tych systemów?
O płatnościach elektronicznych w ramach szkoleń wewnętrznych "Dziel się wiedzą", wypowiedział się Artur Osiński, programista firmy Volanto.
Pytania? Sugestie? Komentarze? Zachęcamy do dyskusji!
This presentation provides an overview of MPEG-DASH and future developments, namely common media application format and virtual reality/360-degree streaming.
The Perfect Storm MPEG DASH with H.265 (HEVC) with HTML5IMTC
Presentation discusses various aspects of IPTV delivery and relationship with H.265 (HEVC), HTML5 and other latest technologies.
Presented during IMTC 20th Anniversary Forum in Porto, Portugal
Universal media access as proposed in the late 90s is now closer to reality. Users can generate, distribute and consume almost any media content, anywhere, anytime and with/on any device. A major technical breakthrough was the adaptive streaming over HTTP resulting in the standardization of MPEG-DASH, which is now successfully deployed in most platforms. The next challenge in adaptive media streaming is virtual reality applications and, specifically, omnidirectional (360°) media streaming.
This tutorial first presents a detailed overview of adaptive streaming of both traditional and omnidirectional media, and focuses on the basic principles and paradigms for adaptive streaming. New ways to deliver such media are explored and industry practices are presented. The tutorial then continues with an introduction to the fundamentals of communications over 5G and looks into mobile multimedia applications that are newly enabled or dramatically enhanced by 5G.
A dedicated section in the tutorial covers the much-debated issues related to quality of experience. Additionally, the tutorial provides insights into the standards, open research problems and various efforts that are underway in the streaming industry.
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
This tutorial consists of three main parts. In the first part, we provide a detailed overview of the HTML5 standard and show how it can be used for adaptive streaming deployments. In particular, we focus on the HTML5 video, media extensions, and multi-bitrate encoding, encapsulation and encryption workflows, and survey well-established streaming solutions. Furthermore, we present experiences from the existing deployments and the relevant de jure and de facto standards (DASH, HLS, CMAF) in this space. In the second part, we focus on omnidirectional (360) media from creation to consumption. We survey means for the acquisition, projection, coding and packaging of omnidirectional media as well as delivery, decoding and rendering methods. Emerging standards and industry practices are covered as well. The last part presents some of the current research trends, open issues that need further exploration and investigation, and various efforts that are underway in the streaming industry.
Real-Time Applications are no longer a niche – they are a crucial part of the modern internet and our distributed and socially distanced lives.
WebRTC usage has been growing dramatically with the increased need to work from home and to have physically distant interactions due to coronavirus quarantines globally. Alberto will talk about low latency applications architecture, scalability, higher quality, and usability.
A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Acc...Priti Kana
JP INFOTECH, offering bulk 2014 and 2015 IEEE Project titles for CSE, IT, ECE, EEE final year students. We are guide to give a best projects for your academic and future career.
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...Alpen-Adria-Universität
Multimedia streaming over HTTP has gained momentum with the approval of the MPEG-DASH standard and many research papers evaluated various aspects thereof but mainly within controlled environments. However, the actual behaviour of a DASH client within real-world environments has not yet been evaluated. The aim of this paper is to compare the QoE performance of existing DASH-based Web clients within real-world environments using crowdsourcing. Therefore, we select Google’s YouTube player and two open source implementations of the MPEG-DASH standard, namely DASH-JS from Alpen-Adria-Universitaet Klagenfurt and dash.js which is the official reference client of the DASH Industry Forum. Based on a predefined content con- figuration, which is comparable among the clients, we run a crowdsourcing campaign to determine the QoE of each implementation in order to determine the current state-of- the-art for MPEG-DASH systems within real-world environments. The gathered data and its analysis will be presented in the paper. It provides insights with respect to the QoE performance of current Web-based adaptive HTTP stream- ing systems.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instancesAlpen-Adria-Universität
Video streaming constitutes 65 % of global internet traffic, prompting an investigation into its energy consumption and CO2 emissions. Video encoding, a computationally intensive part of streaming, has moved to cloud computing for its scalability and flexibility. However, cloud data centers’ energy consumption, especially video encoding, poses environmental challenges. This paper presents VEED, a FAIR Video Encoding Energy and CO2 Emissions Dataset for Amazon Web Services (AWS) EC2 instances. Additionally, the dataset also contains the duration, CPU utilization, and cost of the encoding. To prepare this dataset, we introduce a model and conduct a benchmark to estimate the energy and CO2 emissions of different Amazon EC2 instances during the encoding of 500 video segments with various complexities and resolutions using Advanced Video Coding (AVC)
and High-Efficiency Video Coding (HEVC). VEED and its analysis can provide valuable insights for video researchers and engineers to model energy consumption, manage energy resources, and distribute workloads, contributing to the sustainability of cloud-based video encoding and making them cost-effective. VEED is available at Github.
Addressing climate change requires a global decrease in greenhouse gas (GHG) emissions. In today’s digital landscape, video streaming significantly influences internet traffic, driven by the widespread use of mobile devices and the rising popularity of streaming plat-
forms. This trend emphasizes the importance of evaluating energy consumption and the development of sustainable and eco-friendly video streaming solutions with a low Carbon Dioxide (CO2) footprint. We developed a specialized tool, released as an open-source library called GREEM , addressing this pressing concern. This tool measures video encoding and decoding energy consumption and facilitates benchmark tests. It monitors the computational impact on hardware resources and offers various analysis cases. GREEM is helpful for developers, researchers, service providers, and policy makers interested in minimizing the energy consumption of video encoding and streaming.
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...Alpen-Adria-Universität
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.
In the context of rising environmental concerns, this paper introduces VEEP, an architecture designed to predict energy consumption and CO2 emissions in cloud-based video encoding. VEEP combines video analysis with machine learning (ML)-based energy prediction and real-time carbon intensity, enabling precise estimations of CPU energy usage and CO2 emissions during the encoding process. It is trained on the Video Complexity Dataset (VCD) and encoding results from various AWS EC2 instances. VEEP achieves high accuracy, indicated by an 𝑅2-score of 0.96, a mean absolute error (MAE) of 2.41 × 10−5, and a mean squared error (MSE) of 1.67 × 10−9. An important finding is the potential to reduce emissions by up to 375 times when comparing cloud instances and their locations. These results highlight the importance of considering environmental factors in cloud computing.
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 effi- ciently 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
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.
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...Alpen-Adria-Universität
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.
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.
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.
In the last decades, video streaming has been developing significantly. Among cur- rent 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 StrategiesAlpen-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 LearningAlpen-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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
A Seamless Web Integration of Adaptive HTTP Streaming
1. DASH-JS: A Seamless Web Integration of
Adaptive HTTP Streaming
Benjamin Rainer, Stefan Lederer, Christopher Müller, and Christian Timmerer
Alpen-Adria-Universität Klagenfurt (AAU) Faculty of Technical Sciences (TEWI) Department of Information
Technology (ITEC) Multimedia Communication (MMC) Sensory Experience Lab (SELab)
http://research.timmerer.com http://blog.timmerer.com
http://dash.itec.aau.at/mailto:christian.timmerer@itec.uni-klu.ac.at
EUSIPCO 2012, Bucharest, Romania
August 27-31, 2012
Acknowledgments. This work was supported in part by the European Commission in the context of the ALICANTE project (FP7-ICT-
248652), SocialSensor (FP7-ICT-287975), and the COST Action IC1003 QUALINET.
2. Introduction
• HTTP streaming very popular for over-the-top (OTT) services leveraging
existing infrastructure
– Efficient video compression
– Content delivery networks (CDNs)
– Adaptive video players
• Many proprietary systems, MPEG-DASH as the emerging standard allowing
for dynamic, adaptive client behavior
– Segment formats based on ISOBMFF and MPEG-2 TS identified by HTTP-URLs
– XML-based Media Presentation Description (MPD)
• HTML5 offers new ways to integrate video and audio in Web
sites/applications
• In this presentation
– How to integrate WebM with MPEG-DASH?
– How to implement MPEG-DASH in JavaScript (DASH-JS)?
– Preliminary evaluation results and [live demo]
2012/08/30 http://dash.itec.aau.at 2
4. Integrating WebM with MPEG-DASH
• What is and why WebM?
– Subset of Matroska, mandates VP8 video and Vorbis audio codecs
– Native support through Google Chromes’ Media Source API
• Easy to integrate within MPEG-DASH thanks to codecs/mimeType attributes
– Use FFMPEG to pack each GOP into one WebM cluster – fulfills requirements of the Media
Source API and enables bitrate switching
– Python script to generate MPD
2012/08/30 http://dash.itec.aau.at 4
5. DASH in JavaScript (DASH-JS)
• Completely implemented in JavaScript – no (3rd
party) plugins required
• Makes use of the Media Source API provided by
Google Chrome
– Newest draft (and implementation?) extended to
ISOBMFF
• Provides time based and byte based buffers
– E.g., use as input for adaptation logics
• Flexible adaptation logics
– Easy to extend existing ones or integrate your own
2012/08/30 http://dash.itec.aau.at 5
7. DASH-JS (cont’d)
• Bandwidth / throughput estimation
– … is done each time a segment is retrieved
– At the beginning the MPD is used to have an educated guess on the
bandwidth
– To bypass proxy caching “no-cache” is set in the HTTP Request Header
(will influence the throughput estimation)
• Representation selection is based on:
w1bn-1 + w2 bm
bn =
w1 + w2
– bn-1 denotes the throughput calculated at the n-1th segment
– bm depicts the throughput measured with the nth segment
– bn is used to decide which representation should be selected
– The weights (w1 and w2) are used to mimic optimistic or pessimistic
behavior
• Simple adaptation logic, easy to extend, modify…
2012/08/30 http://dash.itec.aau.at 7
8. Evaluation Test Bed
Apache
Web Server DASH-JS Client
Network Traffic
Emulation Shaping
100 Mbits Ethernet
• RTT: 50ms
• Segment length: 2s
• Various bitrates: 200kbps – 8Mbps (depending on
the showcase)
• w1=0.7 and w2=1.3 => fast reaction
2012/08/30 http://dash.itec.aau.at 8
10. Showcases
• Sintel Trailer @ 480p
– 5 representation from 200 kbps to 2000 kbps video bitrate
– 128 kbps audio for all representations
– Showcase: http://www-itec.uni-klu.ac.at/dash/js/dashtest.html
– MPD: http://www-itec.uni-klu.ac.at/dash/js/content/sintel_multi_rep.mpd
• Big Buck Bunny @ 480p
– 7 representations from 200 kbps to 4700 kbps video bitrate
– 128 kbps audio for all representations
– Showcase: http://www-itec.uni-klu.ac.at/dash/js/dashtest-bunny.html
– MPD: http://www-itec.uni-klu.ac.at/dash/js/content/bigbuckbunny.mpd
• Big Buck Bunny @ 1080p
– 7 representations from 1000 kbps to 8000 kbps
– 128 kbps audio for all representations
– Showcase: http://www-itec.uni-klu.ac.at/dash/js/dashtest-bunny1080p.html
– MPD: http://www-itec.uni-klu.ac.at/dash/js/content/bigbuckbunny_1080p.mpd
2012/08/30 http://dash.itec.aau.at 10
11. Conclusions
• DASH-JS implements a DASH client in JavaScript
– MPD parsing
– Buffering
– Bandwidth estimation, adaptation logic, request handling
• Flexible architecture, easy to extend, e.g.:
– Add your own buffer model
– Add your own bandwidth estimation, adaptation logic
• Interfaces with the browser through appropriate API
– E.g., Chrome’s Media Source API (WebM, [ISOBMFF])
– Others easy to integrate once available (e.g., Mozilla)
• Open source: http://dash.itec.aau.at
– Feel free to use it, please acknowledge/reference us
B. Rainer, S. Lederer, C. Müller, C. Timmerer, “A Seamless Web Integration of Adaptive HTTP
Streaming”, In Proceedings of the 20th European Signal Processing Conference
2012, Bucharest, Romania, August 2012.
2012/08/30 http://dash.itec.aau.at 11
12. DASH @ AAU/ITEC
http://dash.itec.aau.at/
DASH VLC Plugin
DASHEncoder
libdash
Dataset
DASH-JS
Join this activity, everyone
is invited – get involved in
and exited about DASH!
2012/08/30 http://dash.itec.aau.at 12
13. Acknowledgments
• EC projects for partially funding this activity
– ALICANTE project (FP7-ICT-248652)
• http://www.ict-alicante.eu
– SocialSensor project (FP7-ICT-287975)
• http://www.socialsensor.org
– COST ICT Action IC1003
• QUALINET – European Network on Quality
of Experience in Multimedia Systems and Services
• http://www.qualinet.eu/
• DASH Promoters Group
– http://dashpg.com
• Christopher Müller: VLC Plugin, libdash
• Stefan Lederer: DASHEncoder, dataset, DASH-JS
• Benjamin Rainer: DASH-JS
• Hermann Hellwagner for his advice and feedback
• ISO/IEC MPEG and its participating members for their constructive
feedback during the standardization process
2012/08/30 http://dash.itec.aau.at 13
14. References
• Christopher Müller, Daniele Renzi, Stefan Lederer, Stefano Battista and Christian Timmerer, “Using
Scalable Video Coding for Dynamic Adaptive Streaming Over HTTP in Mobile Environments”, In
Proceedings of the 20th European Signal Processing Conference 2012, Bucharest, Romania, August
27-31, 2012.
• Benjamin Rainer, Stefan Lederer, Christopher Müller and Christian Timmerer, “A Seamless Web
Integration of Adaptive HTTP Streaming”, In Proceedings of the 20th European Signal Processing
Conference 2012, Bucharest, Romania, August 27-31, 2012.
• Stefan Lederer, Christopher Müller and Christian Timmerer, “Peer-Assisted Dynamic Adaptive
Streaming over HTTP – System Design and Evaluation”, In Proceedings of the IEEE International
Packet Video Workshop 2012, Munich, Germany, May 10-11, 2012.
• Christopher Müller, Stefan Lederer and Christian Timmerer, “An Evaluation of Dynamic Adaptive
Streaming over HTTP in Vehicular Environments”, In Proceedings of the 4th ACM Workshop on
Mobile Video, Chapel Hill, North Carolina, February 24, 2012.
• Stefan Lederer, Christopher Müller and Christian Timmerer, “Dynamic Adaptive Streaming over
HTTP Dataset”, In Proceedings of the ACM Multimedia Systems Conference 2012, Chapel Hill, North
Carolina, February 22-24, 2012.
• Christopher Müller and Christian Timmerer, “A VLC Media Player Plugin enabling Dynamic Adaptive
Streaming over HTTP”, In Proceedings of the ACM Multimedia 2011, Scottsdale, Arizona, November
28, 2011.
• Christopher Müller and Christian Timmerer, “A Test-Bed for the Dynamic Adaptive Streaming over
HTTP featuring Session Mobility”, In Proceedings of the ACM Multimedia Systems Conference
2011, San Jose, California, February 23-25, 2011.
• Christian Timmerer and Christopher Müller, “HTTP Streaming of MPEG Media”, In Proceedings of
the Streaming Day 2010, Udine, Italy, September 16-17, 2010.
2012/08/30 http://dash.itec.aau.at 14