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
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingAlpen-Adria-Universität
Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively.
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.
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video StreamingAlpen-Adria-Universität
With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingAlpen-Adria-Universität
Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively.
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.
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video StreamingAlpen-Adria-Universität
With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.
Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.
Case Study: How Cisco Gained Visibility into Network Utilization and Proacti...CA Technologies
For most organizations, network capacity is limited and cost is often one of the top constraints. It’s no exception for a leader in providing innovative IT infrastructure solutions to the world. Like its customers in IT operations centers, it’s also required to deliver expected performance levels within its own infrastructure. Visibility into network utilization and being able to proactively plan for capacity demands is just as crucial. Learn how this global provider of IT infrastructure solutions optimizes bandwidth utilization, avoids unnecessary circuit upgrades, and proactively resolves bandwidth issues that could impact critical business activities.
To learn more about DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Optimising Service Deployment and Infrastructure Resource ConfigurationRECAP Project
This is a presentation delivered by Alec Leckey (Intel) at the 2nd Data Centre Symposium held in conjunction with the National Conference on Cloud Computing and Commerce (http://2018.nc4.ie/) on April 10, 2018 in Dublin, Ireland.
Learn more about the RECAP project: https://recap-project.eu/
Install the Intel Landscaper: https://github.com/IntelLabsEurope/landscaper
Server-based and Network-assisted Solutions for Adaptive Video StreamingEswar Publications
Server-based adaptive video streaming is gaining popularity in recent years. This is because clients (client-based) and in-network devices (network or proxy-based) are not powerful enough to run state of the art adaptation algorithms, for example, traffic shaping and machine learning. When decision making is placed at the server new and exciting possibilities are obtained for next best segment selection. This work highlights server-based solutions to adaptive video streaming. It provides a taxonomy of current state of the art solutions. It then illustrates various approaches used for server-based adaptive video streaming. Advantages and disadvantages are discussed.
Network-assisted or in-network DASH solutions have certain advantages over traditional client-based approaches. It is proposed that the sharing of information would result in better network and client bandwidth estimations.
This measure would ensure better next segment selections. In this paper a novel network-assisted DASH taxonomy is proposed. It consists of cache-based, optimization, rate-quality model, and co-operative elements. Recent approaches using the elements of the taxonomy are illustrated. These approaches show the advantages of using network-assisted entities in DASH-based systems.
COST-EFFECTIVE LOW-DELAY DESIGN FOR MULTI-PARTY CLOUD VIDEO CONFERENCINGnexgentechnology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
In the last decades, video streaming has been developing significantly. Among cur- rent technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side.
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
In the last decades, video streaming has been developing significantly. Among current technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side. The main contributions of this thesis are four-fold:
- We propose Days of Future Past Plus (DoFP+) approach that leverages HTTP/3’s features to upgrade low-quality segments while downloading others.
- This thesis proposes a weighted sum model, namely WISH, to provide a high QoE of the video and allow end users to express their preferences among different parameters, including data usage, stall events, and video quality.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
On Optimizing Resource Utilization in AVC-based Real-time Video StreamingAlpen-Adria-Universität
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
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.
In this deck, Paul Isaacs from Linaro presents: State of ARM-based HPC. This talk provides an overview of applications and infrastructure services successfully ported to Aarch64 and benefiting from scale.
"With its debut on the TOP500, the 125,000-core Astra supercomputer at New Mexico’s Sandia Labs uses Cavium ThunderX2 chips to mark Arm’s entry into the petascale world. In Japan, the Fujitsu A64FX Arm-based CPU in the pending Fugaku supercomputer has been optimized to achieve high-level, real-world application performance, anticipating up to one hundred times the application execution performance of the K computer. K was the first computer to top 10 petaflops in 2011."
Watch the video: https://wp.me/p3RLHQ-lIT
Learn more: https://www.linaro.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
An SDN Based Approach To Measuring And Optimizing ABR Video Quality Of Experi...Cisco Service Provider
Reprinted with permission of NCTA, from the 2014 Cable Connection Spring Technical Forum Conference Proceedings. For more information on Cisco video solutions, visit: http://www.cisco.com/c/en/us/products/video/index.html
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.
Case Study: How Cisco Gained Visibility into Network Utilization and Proacti...CA Technologies
For most organizations, network capacity is limited and cost is often one of the top constraints. It’s no exception for a leader in providing innovative IT infrastructure solutions to the world. Like its customers in IT operations centers, it’s also required to deliver expected performance levels within its own infrastructure. Visibility into network utilization and being able to proactively plan for capacity demands is just as crucial. Learn how this global provider of IT infrastructure solutions optimizes bandwidth utilization, avoids unnecessary circuit upgrades, and proactively resolves bandwidth issues that could impact critical business activities.
To learn more about DevOps solutions from CA Technologies, please visit: http://bit.ly/1wbjjqX
Optimising Service Deployment and Infrastructure Resource ConfigurationRECAP Project
This is a presentation delivered by Alec Leckey (Intel) at the 2nd Data Centre Symposium held in conjunction with the National Conference on Cloud Computing and Commerce (http://2018.nc4.ie/) on April 10, 2018 in Dublin, Ireland.
Learn more about the RECAP project: https://recap-project.eu/
Install the Intel Landscaper: https://github.com/IntelLabsEurope/landscaper
Server-based and Network-assisted Solutions for Adaptive Video StreamingEswar Publications
Server-based adaptive video streaming is gaining popularity in recent years. This is because clients (client-based) and in-network devices (network or proxy-based) are not powerful enough to run state of the art adaptation algorithms, for example, traffic shaping and machine learning. When decision making is placed at the server new and exciting possibilities are obtained for next best segment selection. This work highlights server-based solutions to adaptive video streaming. It provides a taxonomy of current state of the art solutions. It then illustrates various approaches used for server-based adaptive video streaming. Advantages and disadvantages are discussed.
Network-assisted or in-network DASH solutions have certain advantages over traditional client-based approaches. It is proposed that the sharing of information would result in better network and client bandwidth estimations.
This measure would ensure better next segment selections. In this paper a novel network-assisted DASH taxonomy is proposed. It consists of cache-based, optimization, rate-quality model, and co-operative elements. Recent approaches using the elements of the taxonomy are illustrated. These approaches show the advantages of using network-assisted entities in DASH-based systems.
COST-EFFECTIVE LOW-DELAY DESIGN FOR MULTI-PARTY CLOUD VIDEO CONFERENCINGnexgentechnology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
In the last decades, video streaming has been developing significantly. Among cur- rent technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side.
Policy-Driven Dynamic HTTP Adaptive Streaming Player EnvironmentMinh Nguyen
In the last decades, video streaming has been developing significantly. Among current technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side. The main contributions of this thesis are four-fold:
- We propose Days of Future Past Plus (DoFP+) approach that leverages HTTP/3’s features to upgrade low-quality segments while downloading others.
- This thesis proposes a weighted sum model, namely WISH, to provide a high QoE of the video and allow end users to express their preferences among different parameters, including data usage, stall events, and video quality.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
On Optimizing Resource Utilization in AVC-based Real-time Video StreamingAlpen-Adria-Universität
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
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.
In this deck, Paul Isaacs from Linaro presents: State of ARM-based HPC. This talk provides an overview of applications and infrastructure services successfully ported to Aarch64 and benefiting from scale.
"With its debut on the TOP500, the 125,000-core Astra supercomputer at New Mexico’s Sandia Labs uses Cavium ThunderX2 chips to mark Arm’s entry into the petascale world. In Japan, the Fujitsu A64FX Arm-based CPU in the pending Fugaku supercomputer has been optimized to achieve high-level, real-world application performance, anticipating up to one hundred times the application execution performance of the K computer. K was the first computer to top 10 petaflops in 2011."
Watch the video: https://wp.me/p3RLHQ-lIT
Learn more: https://www.linaro.org/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
An SDN Based Approach To Measuring And Optimizing ABR Video Quality Of Experi...Cisco Service Provider
Reprinted with permission of NCTA, from the 2014 Cable Connection Spring Technical Forum Conference Proceedings. For more information on Cisco video solutions, visit: http://www.cisco.com/c/en/us/products/video/index.html
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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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.
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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)
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This slide contains concept about MPLS_VPNs specially L3_VPN protocol, according to the latest version of Cisco books(SP and R&S) and i taught it at IRAN TIC company.
In the next slide, i prepare title about MPLS L3_VPN Services and VPLS (MPLS L2_VPN)
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.
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Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
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It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
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The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
IEEE ICC'22_ LEADER_ A Collaborative Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming.pdf
1. LEADER : A Collaborative Edge- and SDN-Assisted
Framework for HTTP Adaptive Video Streaming
IEEE International Conference on Communications (ICC)
May 2022
reza.farahani@aau.at | https://athena.itec.aau.at/ | https://www.rezafarahani.me
Reza Farahani, Farzad Tashtarian, Christian Timmerer, Mohammad Ghanbari, and Hermann Hellwagner
4. ● Video streaming traffic has become the primary type of traffic over the Internet.
○ It includes 53.72% of the total video traffic over the Internet [1]
● HTTP adaptive streaming (HAS) is one of the prominent technologies that delivers more than 51% of video
streams [1]
Introduction- Video Streaming
4
[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/
5. ✔ Pure client-based adaptation decision can lead clients to imperfect adaptations
◆ based on local parameters
◆ insufficient network information
Introduction- Network-Assisted Video Streaming
5
✔ Network-assisted Video Streaming for HAS:
◆ An in-network component with a broader view of the network is employed to assist video
players in making precise adaptation decisions
✔ Modern Networking paradigms, e.g.., SDN, NFV, edge computing have been
used for designing modern network-assisted frameworks
6. ✔ 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
Introduction-Software-Defined Networking (SDN)
6
https://opennetworking.org/sdn-definition/
7. ✔ It is considered as a complementary technology to SDN
✔ NFV enables Virtual Network Functions (VNFs) to
◆ run over an open hardware platform
◆ Reduce OpEx, CapEx
◆ accelerate innovations
Introduction-Network Function Virtualization (NFV)
7
Router
Switch Load Balancer (LB)
Firewall
Virtualization Layer
VRouter VFirewall
VSwitch VLB
VNF VNF
VNF VNF
8. ✔ It provides storage and compute resources close to end-users at the network's edge, reducing
◆ network latency
◆ bandwidth consumption
✔ Edge servers include limited resources ( computation, storage, and bandwidth)
✔ Incorporating SDN, NFV, and Edge computing with HAS technology could
◆ optimize video streaming traffic and network utilization.
Introduction- Edge Computing
8
10. 10
Motivation
✔ 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
E
d
g
e
12. The proposed framework
12
✔ This paper leverages the SDN, NFV, and edge computing technologies and proposes
◆ LEADER as a coLlaborative Edge- and SDN- Assisted framework for HTTP aDaptive vidEo
stReaming.
✔ LEADER employs VNFs with transcoding capability at the edge of an SDN-enabled network
✔ Edge servers are categorized into Local Edge Servers (LES) and Neighbor Edge Servers (NES)
Edge map
Comp Map
Requests
Cache map
Media
Media
Media
13. Central OptimizationModel
13
✔ SDN controller runs an 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 for fetching each client’s requested content quality level from?
2. What is the optimal approach for answering to the requested quality level, i.e., fetch or transcode?
3. What is the optimal action to reach the requested quality?
14. Minimize Client serving time (i.e., fetching time plus transcoding time)
✔ Action Selection (AS) constraint
✔ Serving Time (ST) constraints
✔ CDN/Origin (CO) constraints
✔ Resource Consumption (RC)
Central OptimizationModel
14
✔ Constraints :
✔ Objective :
15. Distributed Heuristic Approach
15
✔ We propose a lightweight heuristic approach to:
1. remedy the high time complexity of the MILP model
2. Consider a bandwidth allocation strategy for shared links between each edge to other servers
✔ We propose the following time slot structure
22. ✔ We evaluate the performance of LEADER compared to the following baseline systems
◆ Non Edge Collaborative (NECOL)
◆ Default Edge Collaborative (DECOL)
✔ The performance of the aforementioned approaches is evaluated through
◆ ASB: Average Segment Bitrate
◆ AQS: Average Number of Quality Switches
◆ ANS: Average Number of Stalls
◆ ASD: Average Stall Duration
◆ APQ: Average Perceived QoE calculated by ITU-T Rec.P.1203 mode 0
◆ CHR: Cache Hit Ratio
◆ ETR: Edge Transcoding Ratio
◆ BTL: Backhaul Traffic Load
Methods for Comparison and Metrics
22