The document proposes ARARAT, a collaborative edge-assisted framework for HTTP adaptive video streaming. ARARAT uses edge servers and an SDN controller to optimize video quality and network utilization. It formulates the problem as a MILP model to determine the optimal location and approach for serving each request. The framework includes local and fine-grained optimization models to allocate resources like bandwidth across edge servers.
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.
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 In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol.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.
Vignesh V Menon is invited to talk on "Video Coding for HTTP Adaptive Streaming" on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk will introduce the basics of video codecs and highlight the scope of HAS-related research on video encoding.
Invited talk on “Video Coding for HTTP Adaptive Streaming” on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk introduced the basics of video codecs and highlighted the scope of HAS-related research on video encoding.
Time: August 14, 10.00AM-10.30AM (CEST) or 1.30PM- 2.00PM (IST)
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...Minh Nguyen
This document proposes a super-resolution (SR) based approach for HTTP adaptive streaming on mobile devices. It introduces an SR neural network called SR-ABR Net that can run in real-time on mobile GPUs while providing quality comparable to state-of-the-art SR models. It also presents a SR-aware adaptive bitrate (ABR) algorithm called WISH-SR that leverages SR-ABR Net to improve video quality while reducing data usage. The evaluation shows WISH-SR achieves a 43% reduction in data usage compared to other ABR methods while maintaining a high quality of experience score.
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsAlpen-Adria-Universität
5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multimedia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an SFC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in
large-scale scenarios. Finally, we design a large-scale cloud-based testbed, including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users’ QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.
In this contribution, we present selected novel approaches and results of our research work in the \ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.
Abstract: Rapid growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the video coding performance of such services in terms of speed and Quality of Experience (QoE). HTTP Adaptive Streaming (HAS) is today’s de-facto standard to deliver clients the highest possible video quality. In HAS, the same video content is encoded at multiple bitrates, resolutions, framerates, and coding formats called representations. This study aims to (i) provide fast and compression-efficient multi-bitrate, multi-resolution representations, (ii) provide fast and compression-efficient multi-codec representations, (iii) improve the encoding efficiency of Video on Demand (VoD) streaming using content-adaptive encoding optimizations, and (iv) provide encoding schemes with optimizations per-title for live streaming applications to decrease the storage or delivery costs or/and increase QoE.
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.
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.
Vignesh V Menon is invited to talk on "Video Coding for HTTP Adaptive Streaming" on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk will introduce the basics of video codecs and highlight the scope of HAS-related research on video encoding.
Invited talk on “Video Coding for HTTP Adaptive Streaming” on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk introduced the basics of video codecs and highlighted the scope of HAS-related research on video encoding.
Time: August 14, 10.00AM-10.30AM (CEST) or 1.30PM- 2.00PM (IST)
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...Minh Nguyen
This document proposes a super-resolution (SR) based approach for HTTP adaptive streaming on mobile devices. It introduces an SR neural network called SR-ABR Net that can run in real-time on mobile GPUs while providing quality comparable to state-of-the-art SR models. It also presents a SR-aware adaptive bitrate (ABR) algorithm called WISH-SR that leverages SR-ABR Net to improve video quality while reducing data usage. The evaluation shows WISH-SR achieves a 43% reduction in data usage compared to other ABR methods while maintaining a high quality of experience score.
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming ApplicationsAlpen-Adria-Universität
5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multimedia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an SFC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in
large-scale scenarios. Finally, we design a large-scale cloud-based testbed, including 250 HTTP Adaptive Streaming (HAS) players requesting two popular MS applications (i.e., live and VoD), conduct various experiments, and compare its effectiveness with baseline systems. Experimental results illustrate that SARENA outperforms baseline schemes in terms of users’ QoE by at least 39.6%, latency by 29.3%, and network utilization by 30% in both MS services.
In this contribution, we present selected novel approaches and results of our research work in the \ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.
Abstract: Rapid growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the video coding performance of such services in terms of speed and Quality of Experience (QoE). HTTP Adaptive Streaming (HAS) is today’s de-facto standard to deliver clients the highest possible video quality. In HAS, the same video content is encoded at multiple bitrates, resolutions, framerates, and coding formats called representations. This study aims to (i) provide fast and compression-efficient multi-bitrate, multi-resolution representations, (ii) provide fast and compression-efficient multi-codec representations, (iii) improve the encoding efficiency of Video on Demand (VoD) streaming using content-adaptive encoding optimizations, and (iv) provide encoding schemes with optimizations per-title for live streaming applications to decrease the storage or delivery costs or/and increase QoE.
This document discusses adapting dynamic adaptive streaming over HTTP (DASH) to work over information-centric networking (ICN). Some key points:
- DASH and ICN share similarities like client-initiated pulling of content chunks and support for efficient caching and replication.
- Open issues include mapping different naming schemes between DASH and ICN, bandwidth estimation over ICN, and caching efficiency.
- Combining DASH and CCN could provide benefits like efficient in-network caching and ability to request segments from multiple sources.
- Previous work has evaluated DASH over CCN and shown potential improvements from features like segment pipelining and using multiple network interfaces/links.
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.
QoE- and Energy-aware Content Consumption for HTTP Adaptive StreamingDanieleLorenzi6
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
How to Optimize Dynamic Adaptive Video Streaming? Challenges and SolutionsAlpen-Adria-Universität
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.
The document discusses quality of experience (QoE) optimization in live streaming. It describes the Christian Doppler laboratory ATHENA which addresses research challenges of HTTP adaptive streaming and emerging streaming methods. Optimizing live video streaming involves determining optimal solutions for factors like data transmission, function placement, caching, encoding, and quality of experience while considering costs and resources. The document outlines different taxonomy of solutions including convex optimization, meta-heuristic approaches, and machine learning/artificial intelligence techniques and provides examples of recent related publications.
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.
With the recent surge in Internet multimedia traffic, the enhancement and improvement of media players, specifically DASH media players happened at an incredible rate. DASH Media players take advantage of adapting a media stream to the network fluctuations by continuously monitoring the network and making decisions in near real-time. The performance of algorithms that are in charge of making such decisions was often difficult to be evaluated and objectively assessed.
CAdViSE provides a Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. In this talk, I will introduce the CAdViSE framework, its application, and propose the benefits and advantages that it can bring to every web-based media player development pipeline. To demonstrate the power of CAdViSE in evaluating Adaptive Bitrate (ABR) algorithms I will exhibit its capabilities when combined with objective Quality of Experience (QoE) models. For this talk, my team at Bitmovin/ATHENA has selected the ITU-T P.1203 (mode 1) model in order to execute experiments and calculate the Mean Opinion Score (MOS), and better understand the behavior of a set of well-known ABR algorithms in a real-life setting. The talk will display how we tested and deployed our framework using a modular architecture into a cloud infrastructure. This method yields a massive growth to the number of concurrent experiments and the number of media players that can be evaluated and compared at the same time, thus enabling maximum potential scalability. In my team’s most recent experiments, we used Amazon Web Services (AWS) for demonstration purposes. Another awesome feature of CAdViSE that will be discussed here is the ability to shape the test network with endless network profiles. To do so, we used a fluctuation network profile and a real LTE network trace based on the recorded internet usage of a bicycle commuter in Belgium.
CAdViSE produces comprehensive logs for each media streaming experimental session. These logs can then be applied against different goals, such as objective evaluation to stitch back media segments and conduct subjective evaluations afterwards. In addition, startup delays, stall events, and other media streaming defects can be imitated exactly as they happened during the experimental streaming sessions.
1) The document discusses research into improving HTTP adaptive video streaming through network assistance from CDNs and SDNs.
2) It poses research questions about how CDNs and SDNs can provide assistance to HAS clients to improve delivery, and whether client assistance to networks could also be beneficial.
3) The state of the art involves standards for networks to provide information to clients, as well as some research on network-assisted HAS using traditional and SDN-enabled network architectures.
Multi-Criteria Optimization of Content Delivery within the Future Media Internetjbruneauqueyreix
This document summarizes Joachim Bruneau-Queyreix's PhD defense. The defense addressed multi-criteria optimization of content delivery for the future media internet. The document provides background on challenges facing content delivery such as increasing traffic demands and need for improved quality of experience. It also summarizes several proposed solutions explored in the PhD research, including MS-Stream which relies on simultaneous usage of multiple servers, MATHIAS which exploits multiple distributed resources for each client, and a hybrid P2P/CDN solution. The defense evaluated these approaches and their ability to improve reliability, scalability and quality of experience for adaptive streaming over HTTP.
Content_adaptive_video_coding_for_HTTP_Adaptive_Streaming.pdfVignesh V Menon
Abstract:
In today’s dynamic streaming landscape, where viewers access content on various devices and encounter fluctuating network conditions, optimizing video delivery for each unique scenario is imperative. 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. Content-adaptive video coding techniques built upon this analysis involve dynamically 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, which reduces computational overhead and enables efficient resource allocation on the server side. In this light, this dissertation proposes contributions categorized into four classes:
Video complexity analysis: This thesis proposes discrete cosine transform (DCT)-energy-based spatial and temporal complexity features to overcome the limitations of state-of-the-art spatiotemporal complexity features and provide an efficient video complexity analysis regarding accuracy and speed for every video (segment).
Content-adaptive encoding optimizations: This thesis proposes a scene detection algorithm using the video complexity analysis features. Moreover, an intra coding unit depth prediction algorithm is proposed, which limits rate-distortion optimization for each coding tree unit in high efficiency video coding by utilizing the spatial correlation with the neighboring CTUs.
Online per-title encoding optimizations: This thesis proposes dynamic resolution prediction, dynamic framerate prediction, perceptually-aware bitrate ladder prediction, and dynamic encoding preset prediction schemes to advance the landscape of adaptive streaming. By addressing these critical aspects, the research establishes adaptive streaming systems that offer superior quality, efficiency, and user engagement, paving the way for a new era of tailored multimedia experience.
Multi-encoding optimizations: This dissertation comprehensively proposes various multi-rate and multi-encoding schemes in serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the rate-distortion optimization (RDO) process across multiple representations.
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:
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningEkrem Çetinkaya
Thesis Presentation - Ekrem Çetinkaya
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.
White Paper - Modern Video Streaming in the Enterprise - Panopto Video PlatformPanopto
In the past several decades, changes in video technology have frequently occurred through seismic shifts in ecosystem support. The triumph of VHS over Betamax, the subsequent shift from VHS to DVD, and the rise of H.264 have all followed a pattern in which the industry rallies around a technology and solidifies its position in the market.
In 2015, the next sea change is underway. Legacy video streaming protocols built on overlay networks, custom protocols, and specialized servers are giving way to chunked, connectionless, HTTP-based “Modern Streaming.”
Organizations that implement their live and on-demand video infrastructure using Modern Streaming stand to benefit from reductions in cost and network management complexity, and from improvements in scalability and the viewing experience. Because modern video protocols have been built to leverage the architecture of the internet and corporate WANs, they work in concert with organizations’ existing web caching infrastructure and WAN optimization technologies.
For organizations with video infrastructure built on legacy streaming protocols like RTMP, MMS, and RTSP, and organizations that have invested in multicast video communication, Modern Streaming represents an inflection point. Although continued investment in legacy video technology limits near-term disruption, it prolongs an inevitable technology transition, increases the eventual cost of switching, and limits the choice of technology providers who are actively divesting from the technologies.
Learn more! In our latest white paper, Modern Video Streaming in the Enterprise: Protocols, Caching, and WAN Optimization, we’ll take a deeper look into the technical shifts driving the move toward Modern Streaming, including the seven characteristics that make a video streaming protocol modern.
We’ll also look that the new opportunities Modern Streaming presents for organizations to use existing network infrastructure for more scalable, cost-effective video delivery.
Policy-driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
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.).
IEEE ICC'22_ LEADER_ A Collaborative Edge- and SDN-Assisted Framework for HTT...Reza Farahani
1) The document proposes LEADER, a collaborative edge- and SDN-assisted framework for HTTP adaptive video streaming. LEADER employs virtual network functions with transcoding capabilities at network edges to optimize video streaming quality of experience and network utilization.
2) An SDN controller runs an optimization model to determine the optimal location, action, and approach for fetching client-requested video qualities. A lightweight heuristic approach is also proposed.
3) An evaluation using a large-scale testbed of 250 clients, edge servers, and an SDN controller shows that LEADER improves average video bitrate, reduces quality switches and stalls, and increases perceived quality of experience over non-collaborative and default edge approaches. LE
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.
The document proposes SARENA, an architecture that leverages SDN, SFC, and edge computing to efficiently deliver video streams with different QoE requirements. SARENA includes virtual proxy, cache, and transcoding functions distributed across the edge and infrastructure layers. An optimization model and heuristic solve for optimal service function chains and resource allocation to maximize QoE. An evaluation in a large-scale testbed showed SARENA improved users' QoE by 39.6%, latency by 29.3%, and network utilization by 30% compared to baseline approaches. Future work includes reinforcement learning methods and FaaS-enabled solutions.
2. A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming over...AliIssa53
This document provides a survey of rate adaptation techniques for Dynamic Adaptive Streaming over HTTP (DASH). It discusses the evolution of video delivery over IP networks, including early use of UDP/RTP and development of standards like DASH. Rate adaptation is important for DASH to adjust video quality based on changing network conditions. The document categorizes rate adaptation techniques according to the feedback signals used and whether adaptation is done at the client, server, or network. It also reviews studies on measuring video traffic.
This white paper introduces a new peer-assisted approach to video streaming designed to overcome limitations of content delivery networks (CDNs). It explains how peer-to-peer streaming can represent a key advantage for broadcasters by enabling them to scale up, improve quality, and handle traffic peaks while reducing costs and network burden. A case study showed the solution achieved up to 58% peer streaming and ensured continued streaming for 50% of users during a server outage. Peer-assisted streaming optimizes video delivery as demand increases by leveraging growing numbers of viewers to share content.
MMSys'21 - Multi-access edge computing for adaptive bitrate video streamingJesus Aguilar
This document presents research on using multi-access edge computing (MEC) to improve adaptive bitrate video streaming. The research questions examine how MEC can provide network assistance for HTTP adaptive streaming, use radio and client context data to coordinate streaming, enable low-latency predictions, and allow edge node collaboration. The methodology will develop concepts, implement prototypes, and conduct quantitative analysis. Ongoing work includes using common media format to reduce storage needs and an approach that improves adaptation decisions using edge awareness. Future work predicts requests using machine learning and establishes inter-edge node communication for caching.
RICHTER is a hybrid P2P-CDN architecture for low latency live video streaming that employs virtualized edge servers. It addresses challenges in CDN- and HAS-based streaming by leveraging characteristics of P2P networks and CDN systems. RICHTER utilizes peers' resources through a distributed transcoding approach in addition to video transmission. Virtual tracker servers located near base stations direct clients' requests and respond based on fetching content from peers, edge servers, CDN servers or origin server depending on latency. An optimization problem and heuristic approach are proposed to guide system operation and answer research questions on optimal placement, response approach, sufficient resources and seeder replacement.
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This document discusses adapting dynamic adaptive streaming over HTTP (DASH) to work over information-centric networking (ICN). Some key points:
- DASH and ICN share similarities like client-initiated pulling of content chunks and support for efficient caching and replication.
- Open issues include mapping different naming schemes between DASH and ICN, bandwidth estimation over ICN, and caching efficiency.
- Combining DASH and CCN could provide benefits like efficient in-network caching and ability to request segments from multiple sources.
- Previous work has evaluated DASH over CCN and shown potential improvements from features like segment pipelining and using multiple network interfaces/links.
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.
QoE- and Energy-aware Content Consumption for HTTP Adaptive StreamingDanieleLorenzi6
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
How to Optimize Dynamic Adaptive Video Streaming? Challenges and SolutionsAlpen-Adria-Universität
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.
The document discusses quality of experience (QoE) optimization in live streaming. It describes the Christian Doppler laboratory ATHENA which addresses research challenges of HTTP adaptive streaming and emerging streaming methods. Optimizing live video streaming involves determining optimal solutions for factors like data transmission, function placement, caching, encoding, and quality of experience while considering costs and resources. The document outlines different taxonomy of solutions including convex optimization, meta-heuristic approaches, and machine learning/artificial intelligence techniques and provides examples of recent related publications.
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.
With the recent surge in Internet multimedia traffic, the enhancement and improvement of media players, specifically DASH media players happened at an incredible rate. DASH Media players take advantage of adapting a media stream to the network fluctuations by continuously monitoring the network and making decisions in near real-time. The performance of algorithms that are in charge of making such decisions was often difficult to be evaluated and objectively assessed.
CAdViSE provides a Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. In this talk, I will introduce the CAdViSE framework, its application, and propose the benefits and advantages that it can bring to every web-based media player development pipeline. To demonstrate the power of CAdViSE in evaluating Adaptive Bitrate (ABR) algorithms I will exhibit its capabilities when combined with objective Quality of Experience (QoE) models. For this talk, my team at Bitmovin/ATHENA has selected the ITU-T P.1203 (mode 1) model in order to execute experiments and calculate the Mean Opinion Score (MOS), and better understand the behavior of a set of well-known ABR algorithms in a real-life setting. The talk will display how we tested and deployed our framework using a modular architecture into a cloud infrastructure. This method yields a massive growth to the number of concurrent experiments and the number of media players that can be evaluated and compared at the same time, thus enabling maximum potential scalability. In my team’s most recent experiments, we used Amazon Web Services (AWS) for demonstration purposes. Another awesome feature of CAdViSE that will be discussed here is the ability to shape the test network with endless network profiles. To do so, we used a fluctuation network profile and a real LTE network trace based on the recorded internet usage of a bicycle commuter in Belgium.
CAdViSE produces comprehensive logs for each media streaming experimental session. These logs can then be applied against different goals, such as objective evaluation to stitch back media segments and conduct subjective evaluations afterwards. In addition, startup delays, stall events, and other media streaming defects can be imitated exactly as they happened during the experimental streaming sessions.
1) The document discusses research into improving HTTP adaptive video streaming through network assistance from CDNs and SDNs.
2) It poses research questions about how CDNs and SDNs can provide assistance to HAS clients to improve delivery, and whether client assistance to networks could also be beneficial.
3) The state of the art involves standards for networks to provide information to clients, as well as some research on network-assisted HAS using traditional and SDN-enabled network architectures.
Multi-Criteria Optimization of Content Delivery within the Future Media Internetjbruneauqueyreix
This document summarizes Joachim Bruneau-Queyreix's PhD defense. The defense addressed multi-criteria optimization of content delivery for the future media internet. The document provides background on challenges facing content delivery such as increasing traffic demands and need for improved quality of experience. It also summarizes several proposed solutions explored in the PhD research, including MS-Stream which relies on simultaneous usage of multiple servers, MATHIAS which exploits multiple distributed resources for each client, and a hybrid P2P/CDN solution. The defense evaluated these approaches and their ability to improve reliability, scalability and quality of experience for adaptive streaming over HTTP.
Content_adaptive_video_coding_for_HTTP_Adaptive_Streaming.pdfVignesh V Menon
Abstract:
In today’s dynamic streaming landscape, where viewers access content on various devices and encounter fluctuating network conditions, optimizing video delivery for each unique scenario is imperative. 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. Content-adaptive video coding techniques built upon this analysis involve dynamically 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, which reduces computational overhead and enables efficient resource allocation on the server side. In this light, this dissertation proposes contributions categorized into four classes:
Video complexity analysis: This thesis proposes discrete cosine transform (DCT)-energy-based spatial and temporal complexity features to overcome the limitations of state-of-the-art spatiotemporal complexity features and provide an efficient video complexity analysis regarding accuracy and speed for every video (segment).
Content-adaptive encoding optimizations: This thesis proposes a scene detection algorithm using the video complexity analysis features. Moreover, an intra coding unit depth prediction algorithm is proposed, which limits rate-distortion optimization for each coding tree unit in high efficiency video coding by utilizing the spatial correlation with the neighboring CTUs.
Online per-title encoding optimizations: This thesis proposes dynamic resolution prediction, dynamic framerate prediction, perceptually-aware bitrate ladder prediction, and dynamic encoding preset prediction schemes to advance the landscape of adaptive streaming. By addressing these critical aspects, the research establishes adaptive streaming systems that offer superior quality, efficiency, and user engagement, paving the way for a new era of tailored multimedia experience.
Multi-encoding optimizations: This dissertation comprehensively proposes various multi-rate and multi-encoding schemes in serial and parallel encoding scenarios. Furthermore, it introduces novel heuristics to limit the rate-distortion optimization (RDO) process across multiple representations.
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:
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine LearningEkrem Çetinkaya
Thesis Presentation - Ekrem Çetinkaya
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.
White Paper - Modern Video Streaming in the Enterprise - Panopto Video PlatformPanopto
In the past several decades, changes in video technology have frequently occurred through seismic shifts in ecosystem support. The triumph of VHS over Betamax, the subsequent shift from VHS to DVD, and the rise of H.264 have all followed a pattern in which the industry rallies around a technology and solidifies its position in the market.
In 2015, the next sea change is underway. Legacy video streaming protocols built on overlay networks, custom protocols, and specialized servers are giving way to chunked, connectionless, HTTP-based “Modern Streaming.”
Organizations that implement their live and on-demand video infrastructure using Modern Streaming stand to benefit from reductions in cost and network management complexity, and from improvements in scalability and the viewing experience. Because modern video protocols have been built to leverage the architecture of the internet and corporate WANs, they work in concert with organizations’ existing web caching infrastructure and WAN optimization technologies.
For organizations with video infrastructure built on legacy streaming protocols like RTMP, MMS, and RTSP, and organizations that have invested in multicast video communication, Modern Streaming represents an inflection point. Although continued investment in legacy video technology limits near-term disruption, it prolongs an inevitable technology transition, increases the eventual cost of switching, and limits the choice of technology providers who are actively divesting from the technologies.
Learn more! In our latest white paper, Modern Video Streaming in the Enterprise: Protocols, Caching, and WAN Optimization, we’ll take a deeper look into the technical shifts driving the move toward Modern Streaming, including the seven characteristics that make a video streaming protocol modern.
We’ll also look that the new opportunities Modern Streaming presents for organizations to use existing network infrastructure for more scalable, cost-effective video delivery.
Policy-driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
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.).
IEEE ICC'22_ LEADER_ A Collaborative Edge- and SDN-Assisted Framework for HTT...Reza Farahani
1) The document proposes LEADER, a collaborative edge- and SDN-assisted framework for HTTP adaptive video streaming. LEADER employs virtual network functions with transcoding capabilities at network edges to optimize video streaming quality of experience and network utilization.
2) An SDN controller runs an optimization model to determine the optimal location, action, and approach for fetching client-requested video qualities. A lightweight heuristic approach is also proposed.
3) An evaluation using a large-scale testbed of 250 clients, edge servers, and an SDN controller shows that LEADER improves average video bitrate, reduces quality switches and stalls, and increases perceived quality of experience over non-collaborative and default edge approaches. LE
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.
The document proposes SARENA, an architecture that leverages SDN, SFC, and edge computing to efficiently deliver video streams with different QoE requirements. SARENA includes virtual proxy, cache, and transcoding functions distributed across the edge and infrastructure layers. An optimization model and heuristic solve for optimal service function chains and resource allocation to maximize QoE. An evaluation in a large-scale testbed showed SARENA improved users' QoE by 39.6%, latency by 29.3%, and network utilization by 30% compared to baseline approaches. Future work includes reinforcement learning methods and FaaS-enabled solutions.
2. A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming over...AliIssa53
This document provides a survey of rate adaptation techniques for Dynamic Adaptive Streaming over HTTP (DASH). It discusses the evolution of video delivery over IP networks, including early use of UDP/RTP and development of standards like DASH. Rate adaptation is important for DASH to adjust video quality based on changing network conditions. The document categorizes rate adaptation techniques according to the feedback signals used and whether adaptation is done at the client, server, or network. It also reviews studies on measuring video traffic.
This white paper introduces a new peer-assisted approach to video streaming designed to overcome limitations of content delivery networks (CDNs). It explains how peer-to-peer streaming can represent a key advantage for broadcasters by enabling them to scale up, improve quality, and handle traffic peaks while reducing costs and network burden. A case study showed the solution achieved up to 58% peer streaming and ensured continued streaming for 50% of users during a server outage. Peer-assisted streaming optimizes video delivery as demand increases by leveraging growing numbers of viewers to share content.
MMSys'21 - Multi-access edge computing for adaptive bitrate video streamingJesus Aguilar
This document presents research on using multi-access edge computing (MEC) to improve adaptive bitrate video streaming. The research questions examine how MEC can provide network assistance for HTTP adaptive streaming, use radio and client context data to coordinate streaming, enable low-latency predictions, and allow edge node collaboration. The methodology will develop concepts, implement prototypes, and conduct quantitative analysis. Ongoing work includes using common media format to reduce storage needs and an approach that improves adaptation decisions using edge awareness. Future work predicts requests using machine learning and establishes inter-edge node communication for caching.
Similar to USuurey_Presentation__CollaborativeHASSystems.pdf (20)
RICHTER is a hybrid P2P-CDN architecture for low latency live video streaming that employs virtualized edge servers. It addresses challenges in CDN- and HAS-based streaming by leveraging characteristics of P2P networks and CDN systems. RICHTER utilizes peers' resources through a distributed transcoding approach in addition to video transmission. Virtual tracker servers located near base stations direct clients' requests and respond based on fetching content from peers, edge servers, CDN servers or origin server depending on latency. An optimization problem and heuristic approach are proposed to guide system operation and answer research questions on optimal placement, response approach, sufficient resources and seeder replacement.
This document discusses challenges in achieving low latency live streaming in a real-world deployment. It notes that while HTTP adaptive streaming technologies allow for high quality video, supporting multiple streaming standards like DASH, HLS, and MSS increases complexity and latency. The document describes experiments testing how changing video format, segment duration, and DVR window length impact end-to-end latency on a testbed system. Shortening segment duration reduced latency while increasing the DVR window had a negative effect on latency.
CSDN_ CDN-Aware QoE Optimization inSDN-Assisted HTTP Adaptive Video Streaming...Reza Farahani
1) The document presents CSDN, a framework that leverages SDN and NFV to provide network assistance for HTTP adaptive video streaming. It proposes using SDN virtual routers equipped with transcoding capabilities to optimize quality of experience (QoE) based on network conditions and user preferences.
2) An evaluation of CSDN on a testbed with 100 clients showed it improved playback bitrate by 7.5% and reduced quality switches and stalls by 19% compared to state-of-the-art approaches, enhancing user QoE and network utilization.
3) Future work directions include improving edge caching strategies, developing learning-based approaches, and extending an MILP model to optimize transcoding
ACM NOSSDAV'21-ES-HAS_ An Edge- and SDN-Assisted Framework for HTTP Adaptive ...Reza Farahani
The document presents ES-HAS, an edge- and SDN-assisted framework for HTTP adaptive video streaming. ES-HAS leverages SDN and NFV paradigms to provide network assistance for video streaming. It introduces virtual reverse proxy servers at the network edge that employ a novel server/segment selection policy. An evaluation on a large-scale cloud testbed with 60 clients shows that ES-HAS outperforms state-of-the-art approaches in terms of playback bitrate and number of stalls by at least 70% and 40% respectively. Future work directions include extending edge caching and collaboration as well as improving the proposed optimization model.
Basic Security in Routing and SwitchingReza Farahani
This slide covers fundamental of security in R&S and critical things about AAA and secure tunneling by IPSEC.
In this slide that I thought it at TIC company, you can find important terms about security and after that you can enhance your device configuration.
This slide contains fundamental concept about Quality of Service (QoS) technology and various types of Queuing Methods, according to the latest version of Cisco books (CCIE R&S and CCIE SP) and i taught it at IRAN TIC company.
Fundamental of Quality of Service(QoS) Reza Farahani
This slide contains fundamental concept about Quality of Service (QoS) technolog, according to the latest version of Cisco books (CCIE R&S and CCIE SP) and i taught it at IRAN TIC company.In the next slide, i upload advanced topic about this attractive technology.
this slide contains fundamental concept about VPLS protocol, according to the latest version of Cisco books and i taught it at IRAN TIC company.in the next slide, i upload attractive advanced feature about VPLS.
(Some of the pictures in this slide are borrowed from the wonderful site of my good friend Gokhan Kosem)
(www.ipcisco.com)
MPLS L3 VPN allows companies to offer Layer 3 VPN services with advantages like scalability, security, and support for duplicate IP addresses and different network topologies. The key components that enable this are VRF tables on PE routers that separate routing information for each customer to avoid duplicate IP issues, and MP-BGP which customizes VPN routing information using a Route Distinguisher, VPN label, and Route Target to support different VPN topologies. MPLS L3 VPN provides services like multi-homed sites for redundancy, hub-and-spoke networks, internet access with security, and extranets for inter-company communication.
This slide contains basic concept about MPLS and LDP protocol, according to the latest version of Cisco books(SP and R&S) and i taught it at IRAN TIC company.
i will prepare MPLS_VPN and MPLS_QoS and MPLS_TE later.
This slide contains the basic and advanced concept of OSPF routing protocol, according to the latest version of Cisco books, and I presented it at IRAN TIC company. In the next slide, I will upload an attractive advanced feature about OSPF.
The document provides an overview of the Border Gateway Protocol (BGP) including:
- BGP establishes neighbor relationships to exchange routing information between autonomous systems (ASes). It uses path attributes like AS_PATH to choose the best route and prevent routing loops.
- BGP classifies neighbors as internal (iBGP) or external (eBGP) depending on if they are in the same AS or different ASes. iBGP does not modify the AS_PATH while eBGP does.
- Techniques like route reflectors, confederations, and multiprotocol BGP are used to improve scalability within large ASes. Route filtering uses features like prefix-lists, route-maps and regular expressions to control route
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
USuurey_Presentation__CollaborativeHASSystems.pdf
1. Collaborative Edge-Assisted Systems for HTTP Adaptive
Video Streaming
Christian Doppler laboratory ATHENA, Institute for Information Technology, Alpen-Adria-Universität Klagenfurt, Austria
January 6th
, 2023
reza.farahani@aau.at | https://athena.itec.aau.at/ | https://www.rezafarahani.me
Reza Farahani
2. Agenda
● Introduction
● Network-Assisted Video Streaming Solutions
● Modern Networking Paradigms
● ARARAT: A Collaborative Edge-Assisted Framework
for HTTP Adaptive Video Streaming
● Conclusion
4. ● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
5. ● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
Source: Sandvine GIPR Jan. 2022.
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
[2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
6. ● Video streaming is the predominant today’s Internet traffic.
Motivation
1
Streaming video service providers’ share of total video traffic in networks (Source: Ericsson Mobility Report Nov. 2022.)
Mobile data traffic by application category per month (Source: Ericsson Mobility Report Nov. 2022.)
Source: Sandvine GIPR Jan. 2022.
[1] Ericsson, “Mobility Report”, White Paper, November 2022. [Online]. Available: EricssonReport
[2] Sandvine, “The Global Internet Phenomena Report”, White Paper, January 2022. [Online]. Available: SandvineReport
11. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
12. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
13. ● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
HTTP Adaptive Video Streaming (HAS)
5
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
14. HTTP Adaptive Video Streaming (HAS)
6
[1] Sandvine, “The Global Internet Phenamena Report,” White Paper, January 2022. [Online]. Available: https://www.sandvine.com/phenomena
https://bitmovin.com/dynamic-adaptive-streaming-http-mpeg-dash/
Adaptation (ABR) logic is within
the client, not normatively
specified by a standard, subject
to research and development
17. Network-Assisted Video Streaming Solutions (NAVS)
8
SDN NFV MEC
Hybrid Systems
NAVS
Systems
5G/6G Paradigms
Content Delivery Networks
Emerging Protocols
ML/RL supports
SFC
Computing Continuum Facilities
Edge
Cloud
Fog
CMAF
QUIC LL-HAS
CMCD/SD
Transcoding
Multi Paradigms
Hybrid P2P-CDN
Caching
Prefetching
18. 9
Our Network-Assisted Solutions
● R. Farahani, M. Shojafar, C. Timmerer, F. Tashtarian, M. Ghanbari, H. Hellwagner. ARARAT: A Collaborative
Edge-Assisted Framework for HTTP Adaptive Video Streaming. IEEE Transactions on Network and Service
Management (TNSM), 2022.
● R. Farahani, A. Bentaleb, E. Cetinkaya, C. Timmerer, R. Zimmermann, and H. Hellwagner. Hybrid P2P-CDN Architecture
for Live Video Streaming: An Online Learning Approach. IEEE Global Communications Conference (GLOBECOM),
2022.
● R. Farahani, F. Tashtarian, C. Timmerer, M. Ghanbari, H. Hellwagner. LEADER: A Collaborative Edge- and SDN-Assisted
Framework for HTTP Adaptive Video. IEEE International Conference on Communications (ICC), 2022.
● R. Farahani, H. Amirpour, F. Tashtarian, A.Bentaleb, C. Timmerer, H. Hellwagner, and R. Zimmermann. RICHTER: Hybrid
P2P-CDN Architecture for Low Latency Live Video Streaming. ACM Mile-High Video (MHV), 2022.
● R. Farahani, F. Tashtarian, H. Amirpour, C. Timmerer, M. Ghanbari, and H. Hellwagner. CSDN: CDN-Aware QoE
Optimization in SDN-Assisted HTTP Adaptive Video Streaming. IEEE 46th Conference on Local Computer Networks
(LCN), 2021.
● R. Farahani, F. Tashtarian, A. Erfanian, C. Timmerer, M. Ghanbari, and H. Hellwagner. ES-HAS: an edge- and
SDN-assisted framework for HTTP adaptive video streaming. The 31st ACM Workshop on Network and Operating
Systems Support for Digital Audio and Video (NOSSDAV), 2021.
● R. Farahani. CDN and SDN Support and Player Interaction for HTTP Adaptive Video Streaming. The 12th ACM
Multimedia Systems Conference (MMSys), 2021.
● R. Farahani, E. Cetinkaya, C. Timmerer, M. Shojafar, M. Ghanbari, H. Hellwagner. ALIVE: A Latency- and Cost-Aware
Hybrid P2P-CDN Framework for Live Video Streaming. IEEE Transactions on Mobile Computing (TMC).
19. 10
● R. Farahani, A. Bentaleb, C. Timmerer , M. Shojafar, R. Prodan, and H. Hellwagner. SARENA: SFC-Enabled Architecture
for Adaptive Video Streaming Applications. IEEE International Conference on Communications (ICC), 2023.
● R. Farahani, A. Bentaleb, M. Shojafar, C. Timmerer, H. Hellwagner. CP-Steering: CDN- and Protocol-Aware Content
Steering Solution for HTTP Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.
● R. Farahani, V. V Menon, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Transcoding Quality Prediction for
Adaptive Video Streaming. ACM Mile High Video (MHV), 2023.
● V. V Menon, R. Farahani, P. T Rajendran, H. Hellwagner, M. Ghanbari, C. Timmerer. Reduced Reference Transcoding
Quality Prediction for Video Streaming Applications. IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2023.
● S. Chellappa, R. Farahani, R. Bartos, C. Timmerer, H. Hellwagner. Context-Aware HTTP Adaptive Video Streaming
Utilizing QUIC’s Stream Priority. ACM Mile High Video (MHV), 2023.
● A. Bentaleb, R. Farahani, F. Tashtarian, C. Timmerer, H. Hellwagner, R.Zimmermann. Which CDN to Download From? A
Client and Server Strategies. ACM Mile High Video (MHV), 2023.
● R. Shokri Kalan, R. Farahani, E. Karsli, C. Timmerer, and H. Hellwagner. Towards Low Latency Live Streaming:
Challenges in Real-World Deployment. The 13th ACM Multimedia Systems Conference (MMSys), 2022.
● F. Tashtarian, A. Bentaleb, R. Farahani, M. Nguyen, C. Timmerer, H.Hellwagner, and R. Zimmermann. A Distributed
Delivery Architecture for User Generated Content Live Streaming over HTTP. IEEE 46th Conference on Local Computer
Networks (LCN), 2021.
● A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, and H. Hellwagner. On Optimizing Resource Utilization in
AVC-based Real-time Video Streaming. The 6th IEEE Conference on Network Softwarization (NetSoft), 2020.
Our Network-Assisted Solutions
20. 11
● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video
Streaming:
1. LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022)
2. ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022)
[1] DOI: 10.1109/ICC45855.2022.9838949
[2] DOI: 10.1109/TNSM.2022.3210595
Our Network-Assisted Solutions
5G/6G Paradigms SDN NFV MEC
21. 11
● This talk covers the following frameworks as Collaborative Edge-Assisted Systems for HTTP Adaptive Video
Streaming:
➢ LEADER: A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video (IEEE ICC 2022)
➢ ARARAT: A Collaborative Edge-Assisted Framework for HTTP Adaptive Video Streaming (IEEE TNSM 2022)
➢ Hybrid P2P-CDN Architecture for Live Video Streaming: An Online Learning Approach (IEEE GLOBECOM 2022)
Our Network-Assisted Solutions
5G/6G Paradigms
Hybrid Systems
SDN NFV MEC
P2P NFV
CDN MEC
23. ✔ Traditional network architecture:
◆ Complex Network Devices
◆ Management Overhead
◆ Limited Scalability
Software-Defined Networking (SDN)
12
Data Plane
Control Plane
24. ✔ Conventional network architecture:
◆ Complex Network Devices
◆ Management Overhead
◆ Limited Scalability
✔ The control plane (forwarding decision) is decoupled from the
data plane (acts on the forwarding decision):
◆ Centralized Network Controller
◆ Standard communication Interface (OpenFlow)
◆ Programmable Open APIs
Software-Defined Networking (SDN)
12
Source: https://opennetworking.org/sdn-definition/
Data Plane
Control Plane
25. ✔ Complementary technology to SDN
✔ Network Functions Virtual Network Functions (VNFs):
◆ run over an open hardware platform
◆ Reduce OpEx, CapEx
◆ accelerate innovations
Introduction-Network Function Virtualization (NFV)
13
Router
Switch Load Balancer (LB)
Firewall
Virtualization Layer
VRouter VFirewall
VSwitch VLB
VNF VNF
VNF VNF
26. ✔ CDN edge servers:
✔ Multi-access Edge Computing (MEC):
◆ It provides storage and computational resources close to end-users at the network's edge, reducing
● network latency
● bandwidth consumption
◆ Edge servers include limited resources (computational, storage, and bandwidth)
Edge Computing
14
MEC server
gNodeB
Origin server
29. 15
Research Questions (RQs)
✔ How to use edge resources efficiently to optimize users’ QoE and network utilization?
✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes?
✔ How to establish a collaboration between edge servers to use their potential idle resources for
serving HAS clients.
✔ How to design a network-assisted HAS scheme without client-side modification ?
✔ How we can implement and evaluate proposed approach in a large-scale testbed?
SDNN
F
V
HAS
M
E
C
30. 16
System Architecture-- Edge Layer
Edge Layer
✔ Edge Servers:
◆ Local Edge Server (LES)
◆ Neighboring Edge Server (NES)
✔ Edge Functions:
◆ Partial Cache (PC)
◆ Video Transcoder (Tran.)
MEC
NFV
34. 20
✔ The SDN controller runs a Central MILP optimization model to respond to the following key questions:
1. Where is the optimal place (i.e., LES, NESs, CSs, or the origin server) in terms of the minimum serving
time and minimum network cost for fetching each client’s requested content quality level from?
2. What is the optimal approach for responding to the requested quality level (i.e., fetch or transcode)?
Optimization Model
35. 21
✔ Considering all feasible actions (nodes and approaches) for serving requests:
Optimization Model-- Action Tree
36. 22
Problem Formulation
Central MILP Optimization Model
Constraints & Objective Function
✓ Resource Map
✓ Requests
✓ Videos Information
✓ Computational Cost
Optimal action for each request
37. 23
Central MILP Optimization Model
● Minimize total serving times (i.e., fetching time plus transcoding time)
● Minimize total network cost (i.e., bandwidth cost plus computational cost)
✔ Multi-Objective Function :
Transmission Time
Transcoding Time
Serving Time
Network Cost
Computational Cost
Bandwidth Cost
38. 24
Central MILP Optimization Model
✔ Constraint Groups :
● Action Selection (AS) constraint
● Serving Time (ST) constraints
● CDN/Origin (CO) constraints
● Resource Consumption (RC) constraints
● Network Cost (NC) constraints
✔ The proposed MILP model is an NP-hard problem
✔ Considering shared links between edge servers to reach other servers changes
the model to a mixed integer non-linear programming (MINLP) model.
41. 26
Local Optimization Model-- Coarse-Grained (CG)
COM → LOM
✔ The LOM is still suffering from high time complexity
25
42. 27
Fine-Grained I (FG I)-- EFG I
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
LOM → EFG I
43. 28
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
Fine-Grained I (FG I)-- EFG I
✔ What about bandwidth allocation in shared link?
44. 28
✔ Each edge server runs a lightweight heuristic algorithm upon receiving a request.
Fine-Grained I (FG I)-- EFG I
✔ What about bandwidth allocation in shared link?
45. 29
✔ Each edge server
○ runs a lightweight heuristic algorithm upon receiving a request.
○ can inform the SDN controller to run the SDN Fine-Grained (SFG) algorithm to allocate
a new bandwidth value to the other servers.
Fine-Grained II (FG II)-- EFG II
EFG I → EFG II
48. 32
Fine-Grained II (FG II)-- Bandwidth Allocation Strategy
BwAllocation request
Minimum fairness value among
all fairness coefficient.
✔ The bandwidth allocation is modeled as a “Fairness LP Optimization Model”
The fairness coefficient to the shared link(a, b)
in the route between i and j.
The allocated bandwidth to the shared link(a, b)
in the route between i and j.
49. 33
Research Questions (RQs)
✔ How to use edge resources efficiently to optimize users’ QoE and network utilization?
✔ How to design an edge- and SDN-assisted HAS framework for video optimization purposes?
✔ How to establish a collaboration between edge servers to use their potential idle resources
for serving HAS clients.
✔ How to design a network-assisted HAS scheme without client-side modification ?
✔ How we can implement and evaluate proposed approach in a large-scale testbed?
SDNN
F
V
HAS
M
E
C
50. 34
Evaluation Setup
✔ We design a large-scale cloud-based testbed, including 301 nodes (Xen virtual machines):
○ Real network topology Geant and Abilene.
○ 250 DASH clients
○ Four cache servers (Apache server and MongoDB)
○ 40 OpenFlow switches
○ An SDN controller (Floodlight)
○ Five edge servers (each edge server is responsible for 50 clients)
○ A video Dataset including:
■ Fifty video sequences (each video includes 150 segments)
■ 2 seconds segments
■ Three Bitrate ladder
○ BOLA and SQUAD ABR algorithms
○ FFmpeg transcoder
○ Bandwidth monitoring (Floodlight Restful API)
○ LRU cache replacement policy
○ Zipf distribution video access popularity
○ Wondershaper bandwidth allocators
○ Python, Pulp and CPLEX
51. 35
Evaluation Methods
✔ SABR (https://doi.org/10.1145/3083187.3083196):
◆ Non edge-enabled system
◆ Customized DASH players utilize some important resource data (i.e., cache map and bandwidth
map) provided by the SDN controller to make decisions about the next segment requests.
✔ ES-HAS (https://doi.org/10.1145/3458306.3460997):
◆ Non edge-collaborative system
◆ Non transcoding-based system
◆ Each edge runs an MILP model on the collected client requests to serve them via one of actions 1,
7, or 9.
✔ CSDN (10.1109/LCN52139.2021.9524970):
◆ Non-collaborative approach
◆ Each edge server runs an MILP model for the collected client requests and serves them separately
via one of the actions 1, 2, 7, 8 or 9.
✔ NECOL:
◆ The Non Edge Collaborative (NECOL) system does not support an edge collaboration.
◆ Each NECOL edge server executes a simplified version of the proposed FG approach I for
◆ each client request to serve it through one of the actions 1, 2, 7, 8 or 9 (Fig. 1).
52. 36
Evaluation Results-- Scenario I
✔ This scenario compares the proposed centralized optimization MILP model and the CG and FG
approaches in terms of:
◆ ETV: Execution Time Values for the different ARARAT schemes.
◆ NOV: Normalized Objective Value for the ARARAT schemes.
53. 37
Evaluation Results-- Scenario I
✔ This scenario compares the proposed centralized optimization MILP model and the CG and FG
approaches in terms of:
◆ ETV: Execution Time Values for the different ARARAT schemes.
◆ NOV: Normalized Objective Value for the ARARAT schemes.
54. 38
Evaluation Results-- Scenario I
✔ This scenario compares the proposed centralized optimization MILP model and the CG and FG
approaches in terms of:
◆ ETV: Execution Time Values for the different ARARAT schemes.
◆ NOV: Normalized Objective Value for the ARARAT schemes.
55. 39
Evaluation Results-- Scenario I
✔ This scenario compares the proposed centralized optimization MILP model and the CG and FG
approaches in terms of:
◆ ETV: Execution Time Values for the different ARARAT schemes.
◆ NOV: Normalized Objective Value for the ARARAT schemes.
56. 40
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ ASB: Average Segment Bitrate of all downloaded segments.
57. 41
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ AQS: Average Quality Switches, i.e., the number of segments whose bitrate levels change compared to the
previous ones.
58. 42
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ ASD: Average Stall Duration, i.e., the average of total video freeze times in all clients.
◆ ANS: Average Number of Stalls, i.e., the average number of rebuffering events.
59. 43
Evaluation Results-- Scenario II
✔ This scenario studies the performance of the proposed ARARAT CG and FG schemes on the testbed and
compare the QoE results with state-of-the-art methods:
◆ APQ: Average Perceived QoE, calculated by ITU-T Rec. P.1203 mode 0 [1]
[1] https://www.itu.int/net4/ipr/details_ps.aspx?sector=ITU-T&id=P1203-01
60. 44
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ CHR: Cache Hit Ratio, defined as the fraction of segments fetched from the CDN or edge servers.
◆ ETR: Edge Transcoding Ratio, i.e., the fraction of segments transcoded at the edge servers.
61. 45
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ BTL: Backhaul Traffic Load, the volume of segments downloaded from the origin server.
◆ ANU: Average Network Utilization per link, i.e., κl/Kl, where κl and Kl represent the measured traffic (in bit/s)
on link l and the total allocated bandwidth to link l, respectively.
62. 46
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ AST: Average Serving Time for all clients, including fetching time plus transcoding time.
NCV: Network Cost Values, including computational and bandwidth costs.
63. 47
Evaluation Results-- Scenario III
✔ This scenario investigates the performance of the proposed ARARAT CG and FG schemes in terms of network
utilizations metrics and compare results with other frameworks:
◆ ANC: Average Number of Communicated messages from/to the SDN controller to/from all clients (in the
◆ SABR method) or all edge servers (in other frameworks), including OF and HTTP messages.
65. ● A novel edge-collaborative system for HAS called ARARAT
● ARARAT leverages the 5G/6G paradigms (i.e., SDN, NFV, MEC) to propose a framework for
serving HAS clients with minimum serving latency and networking cost
● We design a multi-layer architecture and formulate the problem as a central
optimization model
● We propose three heuristic approaches to make our framework practical in large-scale
scenarios
● We designed and instantiate a large-scale testbed consisting of 250 clients and
conducts experiments for validating our solutions
● ARARAT approaches outperforms state-of-the-art schemes in terms of users’ QoE,
network cost and the network utilization by at least 47%, 47% and 48%, respectively
Conclusion and Future Work
48
66. ● Augmenting ARARAT with new components to support the following features is some of
our future directions:
○ a RL-based agent
○ CMCD and CMSD communications
○ Multi-stack protocols
○ Auto-configuration nodes
Conclusion and Future Work
49