Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
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Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the MONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.
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Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...Alpen-Adria-Universität
HTTP/2 has been explored widely for video streaming, but still suffers from Head-of-Line blocking and three-way hand-shake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of quick and HTTP/2 on the performance of adaptive bitrate (ABR) algorithms in terms of different metrics. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and also provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High EfficiencyVideo Coding (SHVC).HTTP Adaptive Streaming State of the Art and Challenges Ahead
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.
Delivering Traditional and Omnidirectional Media
This document provides an overview of an IEEE ICME tutorial on streaming media delivery held in San Diego in July 2018. The tutorial covered principles of HTTP adaptive streaming and omnidirectional media delivery. It discussed content generation, distribution and consumption workflows for traditional and omnidirectional media. Standards and emerging technologies in adaptive streaming were also covered. The second part of the tutorial focused on 5G radio access and core networks as well as emerging multimedia use cases. Presenters included researchers and industry experts from academia and companies like Cisco, InterDigital and Bitmovin.
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.Bitmovin LIVE Tech Talks: Analytics for Workflow Automation (ft. Touchstream ...
As part of Bitmovin's NAB 2020 Virtual event series, we were joined by live video monitoring solutions provider Touchstream Media and had the chance to discuss how live-streaming organizations (such as Sports broadcasters) should automate analytics and data to best improve your video workflows.
View our on-demand discussion featuring case studies from a few major sports broadcasters: https://go.bitmovin.com/techtalk-live-analytics-automation-touchstream?utm_source=slideshare
"Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product," a Pres...
"Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product," a Pres...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-cocoonhealth
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pavan Kumar, Co-founder and CTO of Cocoon Health (formerly Cocoon Cam), delivers the presentation "Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Kumar explains how his company is evolving its use of edge and cloud vision computing in continuing to bring new capabilities to the product category of baby monitors.More Related Content
What's hot
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
HTTP Adaptive Streaming (HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behavior according to changing network conditions, it may result in video quality variations that negatively impact the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization- based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both the next segment to be requested and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., retransmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9%.
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...Alpen-Adria-Universität
Adaptive BitRate (ABR) algorithms play a crucial role in delivering the highest possible viewer’s Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS – the dominant video streaming technique on the Internet – to deliver the best QoE for their users. Viewer’s delightfulness relies heavily on how the ABR of a media player can adapt the stream’s quality to the current network conditions. QoE for end-to-end video streaming sessions has been evaluated in many research projects to give better insight into the quality metrics. Objective evaluation models such as ITU Telecommunication Standardization Sector (ITU-T) P.1203 allow for the calculation of Mean Opinion Score (MOS) by considering various QoE metrics, and subjective evaluation is the best assessment approach in investigating the end-user opinion over a video streaming session’s experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper’s main contribution is subjective evaluation analogy with objective evaluation for well-known heuristic-based ABRs.CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video StreamingAlpen-Adria-Universität
With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.
PEMWN'21 - ANGELA
ANGELA is an HTTP adaptive streaming and edge computing simulator that was developed to address limitations of other simulators. It focuses on video streaming and edge computing mechanisms. ANGELA imports real radio traces or data from NS-3 for accurate wireless simulations and reduces simulation time. It also simulates various adaptive bitrate algorithms located at the client, edge, or server. ANGELA provides metrics to evaluate streaming quality and supports custom video datasets and machine learning techniques. The developers hope to publicly release ANGELA to evaluate new adaptive streaming and edge computing approaches.
Video complexity analyzer (VCA) for streaming applications
Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...Alpen-Adria-Universität
Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7×.A Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...Alpen-Adria-Universität
Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA(joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers(ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance ofQuaLAin achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutionsOn Optimizing Resource Utilization in AVC-based Real-time Video Streaming
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
20 Years of Streaming in 20 Minutes
Streaming media has evolved significantly over the past 20 years. Early systems in the 1990s used proprietary protocols over UDP and later included pre-roll buffers and adaptive bitrate techniques. Standards like RTSP, 3GPP, and ISMA provided interoperability but relied on complex server implementations. The shift to HTTP in the 2000s simplified delivery using progressive download and then adaptive streaming formats like HLS, DASH, and CMAF that divide media into short segments. These standards separate the media format from the delivery method, enabling delivery via HTTP while supporting features like DRM and playback across different devices and networks.
LwTE: Light-weight Transcoding at the Edge
Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain.
SLFC: Scalable Light Field Coding
Light field imaging enables some post-processing capabilities like refocusing, changing view perspective, and depth estimation. As light field images are represented by multiple views they contain a huge amount of data that makes compression inevitable. Although there are some proposals to efficiently compress light field images, their main focus is on encoding efficiency. However, some important functionalities such as viewpoint and quality scalabilities, random access, and uniform quality distribution have not been addressed adequately. In this paper, an efficient light field image compression method based on a deep neural network is proposed, which classifies multiple views into various layers. In each layer, the target view is synthesized from the available views of previously encoded/decoded layers using a deep neural network. This synthesized view is then used as a virtual reference for the target view inter-coding. In this way, random access to an arbitrary view is provided. Moreover, uniform quality distribution among multiple views is addressed. In higher bitrates where random access to an arbitrary view is more crucial, the required bitrate to access the requested view is minimized.
Quality impact of scalable video coding tunneling for media aware content del...
Quality impact of scalable video coding tunneling for media aware content del...Alpen-Adria-Universität
The document discusses a study on using Scalable Video Coding (SVC) tunneling for media-aware content delivery. SVC can save bandwidth for multicast content delivery but many devices do not support SVC natively. The study proposes using SVC tunneling only for delivery, with transcoding at ingress and egress points. The study tests transcoding MPEG-2 video to SVC and back to MPEG-2, finding a peak signal-to-noise ratio drop of 2.1 dB and 43% lower bandwidth requirements compared to MPEG-2 simulcast. Future work could analyze different video formats and implementations of SVC.H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...Alpen-Adria-Universität
HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.What’s new in MPEG?
The document summarizes an upcoming webinar on new developments in MPEG standards. It will discuss Versatile Video Coding (VVC), MPEG-H 3D Audio Baseline Profile, video-based point cloud compression (V-PCC), and MPEG Immersive Video (MIV). The webinar will provide overviews of each standard and their applications, as well as results from recent verification tests that evaluated subjective quality and performance. Speakers will include leaders from MPEG working groups and the Joint Video Experts Team.
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...Alpen-Adria-Universität
HTTP/2 has been explored widely for video streaming, but still suffers from Head-of-Line blocking and three-way hand-shake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of quick and HTTP/2 on the performance of adaptive bitrate (ABR) algorithms in terms of different metrics. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and also provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High EfficiencyVideo Coding (SHVC).HTTP Adaptive Streaming State of the Art and Challenges Ahead
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.
Delivering Traditional and Omnidirectional Media
This document provides an overview of an IEEE ICME tutorial on streaming media delivery held in San Diego in July 2018. The tutorial covered principles of HTTP adaptive streaming and omnidirectional media delivery. It discussed content generation, distribution and consumption workflows for traditional and omnidirectional media. Standards and emerging technologies in adaptive streaming were also covered. The second part of the tutorial focused on 5G radio access and core networks as well as emerging multimedia use cases. Presenters included researchers and industry experts from academia and companies like Cisco, InterDigital and Bitmovin.
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.What's hot (20)
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Video complexity analyzer (VCA) for streaming applications
Video complexity analyzer (VCA) for streaming applications
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
A Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
Quality impact of scalable video coding tunneling for media aware content del...
Quality impact of scalable video coding tunneling for media aware content del...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges Ahead
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
Similar to Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
Bitmovin LIVE Tech Talks: Analytics for Workflow Automation (ft. Touchstream ...
As part of Bitmovin's NAB 2020 Virtual event series, we were joined by live video monitoring solutions provider Touchstream Media and had the chance to discuss how live-streaming organizations (such as Sports broadcasters) should automate analytics and data to best improve your video workflows.
View our on-demand discussion featuring case studies from a few major sports broadcasters: https://go.bitmovin.com/techtalk-live-analytics-automation-touchstream?utm_source=slideshare
"Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product," a Pres...
"Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product," a Pres...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-cocoonhealth
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Pavan Kumar, Co-founder and CTO of Cocoon Health (formerly Cocoon Cam), delivers the presentation "Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Kumar explains how his company is evolving its use of edge and cloud vision computing in continuing to bring new capabilities to the product category of baby monitors.Ojoconsulting Oy Nimbus Monitoring Service description v1.2 public
The document discusses corporate service management challenges and introduces NimbusMonitoring Service. It notes that monitoring infrastructure and services is complicated due to many information sources and dependencies. The service aims to offer simple traffic light status overviews and correlate monitoring across network, server, and quality metrics. It also seeks to automate recovery processes and control cloud costs through monitoring.
Overcoming online video streaming challenges with better Quality-of-Experience
Overcoming online video streaming challenges with better Quality-of-ExperienceHughes Systique Corporation
The new-age viewers desire the same level of quality for online streaming as they get with the conventional cable or satellite services. To overcome challenges in the OTT (Over-the-top content) environment, offering an outstanding quality of experience (QoE) has become crucial for the operators. They need to define and monitor a few metrics that revolve around the quality of experience for online video streaming. How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
Empowered by today’s rich tools for media generation and collaborative production and convenient network access to the Internet, video streaming has become very popular. Dynamic adaptive video streaming is a technique used to deliver video content to users over the Internet, where the quality of the video adapts in real time based on the network conditions and the capabilities of the user’s device. HTTP Adaptive Streaming (HAS) has become the de-facto standard to provide a smooth and uninterrupted viewing experience, especially when network conditions frequently change. Improving the QoE of users concerning various applications‘ requirements presents several challenges, such as network variability, limited resources, and device heterogeneity. For example, the available network bandwidth can vary over time, leading to frequent changes in the video quality. In addition, different users have different preferences and viewing habits, which can further complicate live streaming optimization. Researchers and engineers have developed various approaches to optimize dynamic adaptive streaming, such as QoE-driven adaptation, machine learning-based approaches, and multi-objective optimization, to address these challenges. In this talk, we will give an introduction to the topic of video streaming and point out the significant challenges in the field. We will present a layered architecture for video streaming and then discuss a selection of approaches from our research addressing these challenges. For instance, we will present approaches to improve the QoE of clients in User-generated content applications in centralized and distributed fashions. Moreover, we will present a novel architecture for low-latency live streaming that is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches.
PLNOG 17 - Stefan Meinders - Slow is the new Down
In this presentation, we will review trends in Over-the-Top (OTT) traffic usage, user behavior and rapidly growing volumes of video streaming from services such as Netflix. The amount of video traffic carried by networks and its interconnections is significant. Delivery methods and encryption are challenges for today’s methods of network analysis—as are unexpected network changes that can drastically impact subscribers, transit costs, and network overload. Recent research unveiled that existing monitoring tools cannot cope with these variations in traffic delivery, caching strategies, and encryption. Finally, we describe how the use of “big data” techniques and multi-dimensional databases combined with global internet service mapping provides new insights into traffic delivery, consumption, and quality.
Virtual STB / Cloud UI Streaming revisited
Virtual stb revisited
latest thinking about cloud browser ui streaming
Dr. Randolph Nikutta
Telekom Innovation Laboratories
Connected TV World Summit, London, May 2017
1) The document discusses the latest thinking around virtual set-top boxes (vSTBs) and cloud browser user interface (UI) streaming for connected TV.
2) It describes how early skeptics had myths around the technical and financial feasibility of vSTBs, but shifting trends in technology, business needs, and customer demands have increased acceptance.
3) Successful deployments by cable operators around the world demonstrate the technical feasibility of delivering an enriched TV experience through cloud-rendered UIs that provide faster innovation and reduced costs compared to
Join the Revolution: The Interconnected World with IBM Bluemix and IoT Founda...
This deck describes the basics of Bluemix, how it is a true developer-friendly cloud, how you can use Bluemix for IoT development, and leads to an IoT demo developed on Bluemix.
Digiturk_TV_Connect_2015
OTT video streaming is growing rapidly and placing increasing demands on network infrastructure. Providing a high quality viewing experience is critical for customer loyalty and retention. The top technical challenges for OTT providers are quality of service and bandwidth limitations. Using a private CDN and adaptive bitrate streaming can help address these challenges by providing more edge caching and control over content delivery. However, technology alone is not sufficient - having a good pricing strategy, large content library, customer service, and listening to customers are also important.
Монетизация сетевой инфраструктуры
Презентация Юрия Коваленко, Solution Consultant, Opera Software, на конференции F5 Agility Kyiv (27/11/2014)
IEEEGlobecom'22-OL-RICHTER.pdf
This document proposes a hybrid P2P-CDN architecture called RICHTER for live video streaming. RICHTER leverages NFV and edge computing to employ virtual transcoding servers that optimize content delivery by intelligently selecting whether to fetch or transcode content from peers, CDNs or the origin server. An online learning approach is used to solve the NP-hard optimization problem. Evaluation on a large-scale testbed shows RICHTER improves QoE, latency and network utilization compared to baseline schemes. Future work includes extending the action classification tree.
Web Information Systems and Technologies 2009
Integration between Digital Terrestrial Television and Internet by means of a DVB-MHP web browser
Irene Amerini, Roberto Caldelli, Rudy Becarelli, Francesco Filippini, Giovanni Ballocca, Roberto Borri)
Lisbona 24 March 2009
Best practices for live streaming
The document provides best practices for live streaming including:
1) Making live streaming architectures resilient by using multiple CDNs and redundant encoding and distribution infrastructure.
2) Investing in end-to-end monitoring by defining metrics across all components and building a standardized data pipeline.
3) Improving user experience through techniques like using bitrate ladders, device-aware manifests for different devices, and typically using HLS over DASH for live streaming.
Set up box can become home IoT server
To embed our software in your set top box to make it becoming the home server for the consumer IoT system.
Bluemix Local – Relay Options and Challenges
IBM's Bluemix Local provides customers the ability to deploy Bluemix applications and services behind their own firewall. The presentation discusses:
1) Bluemix Local uses an SSL tunnel originating from an inception VM at the customer site to securely connect the customer environment to IBM's infrastructure for platform deployment and maintenance.
2) Customers benefit from 24/7 monitoring of the local Bluemix environment by IBM as well as security management services like patching and threat assessment.
3) A customer example from Spain is discussed where the customer chose Bluemix Local to keep their Cloud Foundry environment up-to-date while also leveraging additional IBM services behind their firewall.
4) Common challenges like monitoring the SSL tunnel
Ultra-High-Definition Quality of Experience with MPEG-DASH
This document discusses quality of experience (QoE) for video streaming using MPEG-DASH. It introduces DASH and key QoE factors like startup delay, buffering, and quality switches. The document describes evaluating different DASH clients both objectively by measuring metrics like stalls, and subjectively through user studies. It finds that while various factors influence QoE for DASH, minimizing stalls and maintaining high throughput and low switching are generally important. The document concludes that continued research is needed to better understand DASH QoE.
Audi - TCU Project - H Schumacher
The document discusses an Audi telematics project in collaboration with IBM. IBM provided the overall architecture, components for the telematics control unit, and project management. The goal was to develop an end-to-end telematics prototype integrating the vehicle with external content and applications through a wireless gateway. IBM's architecture offered flexibility, security, and integration of the vehicle with the internet and wireless networks. The project helped build the foundation for Audi's telematics platform and global leadership position.
2307 - DevBCN - Otel 101_compressed.pdf
This document discusses OpenTelemetry, an open source project for collecting and managing traces, metrics, and logs in a vendor-agnostic way. It provides an overview of distributed tracing and explains why distributed traces are useful for root cause analysis. It also describes how to enable distributed tracing by instrumenting services, using a collector, and configuring pipelines in the OpenTelemetry Collector to receive, process, and export telemetry data.
“Seamless Deployment of Multimedia and Machine Learning Applications at the E...
“Seamless Deployment of Multimedia and Machine Learning Applications at the E...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/06/seamless-deployment-of-multimedia-and-machine-learning-applications-at-the-edge-a-presentation-from-qualcomm/
Megha Daga, Senior Director of Product Management for AIoT at Qualcomm, presents the “Seamless Deployment of Multimedia and Machine Learning Applications at the Edge” tutorial at the May 2022 Embedded Vision Summit.
There has been an explosion of opportunities for edge compute solutions across the internet of things. This growth in opportunities and the diversity of applications is leading to fragmentation in the IoT space both in hardware and software, which creates challenges for developers. In addition, customers and developers are facing challenges in efficient data management and optimized application deployment on embedded edge platforms.
In this session, Daga introduces the Qualcomm Intelligent Multimedia SDK, which empowers developers to tackle these challenges and deploy edge compute applications in a scalable, flexible and optimized way. The Qualcomm Intelligent Multimedia SDK easily decodes and organizes sensor data and executes applications efficiently on edge platforms.What's New in IBM Streams V4.2
Mike Spicer is the lead architect for IBM Streams. In this presentation, Mike covers the key new features available in IBM Streams V4.2.
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"Edge/Cloud Tradeoffs and Scaling a Consumer Computer Vision Product," a Pres...
Ojoconsulting Oy Nimbus Monitoring Service description v1.2 public
Ojoconsulting Oy Nimbus Monitoring Service description v1.2 public
Overcoming online video streaming challenges with better Quality-of-Experience
Overcoming online video streaming challenges with better Quality-of-Experience
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
Join the Revolution: The Interconnected World with IBM Bluemix and IoT Founda...
Join the Revolution: The Interconnected World with IBM Bluemix and IoT Founda...
Ultra-High-Definition Quality of Experience with MPEG-DASH
Ultra-High-Definition Quality of Experience with MPEG-DASH
“Seamless Deployment of Multimedia and Machine Learning Applications at the E...
“Seamless Deployment of Multimedia and Machine Learning Applications at the E...
More from Alpen-Adria-Universität
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Video Streaming: Then, Now, and in the Future
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 instances
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.
GREEM: An Open-Source Energy Measurement Tool for Video Processing
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 ...
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.VEEP: Video Encoding Energy and CO₂ Emission Prediction
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.
Content-adaptive Video Coding for HTTP Adaptive Streaming
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...
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...
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...
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...
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...
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
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...
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
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
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...
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
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesAlpen-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, ...
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
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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Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
GREEM: An Open-Source Energy Measurement Tool for Video Processing
GREEM: An Open-Source Energy Measurement Tool for Video Processing
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
Optimal Quality and Efficiency in Adaptive Live Streaming with JND-Aware Low ...
VEEP: Video Encoding Energy and CO₂ Emission Prediction
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Content-adaptive Video Coding for HTTP Adaptive Streaming
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Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
Optimizing Video Streaming for Sustainability and Quality: The Role of Prese...
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Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
Machine Learning Based Resource Utilization Prediction in the Computing Conti...
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Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
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Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
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VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
Energy Consumption in Video Streaming: Components, Measurements, and Strategies
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Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Astute Business Solutions | Oracle Cloud Partner |
Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Apps Break Data
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before the computed tomography, it had been widely used.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
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8. Monitoring Application Metrics with Prometheus
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10. Configuring Camel K Integrations for Data Pipelines
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12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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- 1. Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks Cise Midoglu1 , Anatoliy Zabrovskiy2 , Özgü Alay3 , Daniel Hölbling-Inzko4 , Carsten Griwodz5 , Christian Timmerer2,4 1 Simula Research Laboratory, 2 University of Klagenfurt, 3 Simula Metropolitan Center for Digital Engineering, 4 Bitmovin Inc, 5 University of Oslo OS-05
- 2. Motivation ● Video streaming one of the top traffic contributors in the Internet ● Mobile broadband (MBB) networks becoming more and more prominent ● Metadata-rich measurement result collection is challenging ● Over-the-top (OTT) video analytics are not used widely in research OS-05 2
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- 4. Overview ● Automatised measurement and evaluation framework for video streaming Quality of Experience (QoE) ○ End-to-end with client and server side ○ Client-initiated active measurements ○ Docker virtualization → platform compatibility ○ Headless streaming → focus on objective QoE ○ Allows to monitor and collect data from multiple layers (application, transport, network, physical) ○ Allows to benchmark video players, ABR algorithms, networks, operators, ... OS-05 4
- 5. Client Side ● Designed to emulate real user initiating a video streaming session ● Selenium instrumentation opens headless browser and streams from HTTP server ● Implemented as Docker container ● Compatible with mobile broadband testbeds (e.g., MONROE) ● Configurable (network, player, ABR algorithm, run duration, batch experimentation, ...) ● Additional network monitoring (ping + traceroute), and metadata if available OS-05 5
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- 10. Server Side OS-05 10 ● All pages are integrated with Bitmovin Analytics web collector v2.3 ○ Possible to view metrics in real-time from the Bitmovin Dashboard, or export in raw format using the Bitmovin Analytics Export Service
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- 15. Demo OS-05 15
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