Semester Opening WS'10/'11

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Alpen-Adria-Universität

Semester Opening WS’10/’11 H. Hellwagner & Christian Timmerer Klagenfurt University (UNI-KLU)  Faculty of Technical Sciences (TEWI) Department of Information Technology (ITEC)  Multimedia Communication (MMC) http://www.uni-klu.ac.at/tewi/inf/itec/mmc/  https://www-itec.uni-klu.ac.at/mmc/blog/ Multimedia Communication + Computer and Network Architectures

Spezialisierungsfach „Rechner- und Netzarchitektur“ („Computer and Network Architectures“) Prof. Hellwagner Lehrveranstaltungen LV-Leiter LV-Termine Current Topics in Computer and Network Architectures: Multimedia Communication and Internet QoS (2 VK) Hellwagner WS 2010/11 Current Topics in Computer and Network Architectures: ... (2 VK) Hellwagner SS 2011 Seminar in Computer and Network Architectures (2 SE) Hellwagner SS 2011 Selected Topics in Computer and Network Architectures: Multimedia Interactive Applications: Creation, Delivery, and Visualization (2 VK) Concolato WS 2010/11 Selected Topics in Distributed Multimedia Systems: Systems for Multimedia Applications (2 VK) - To be conf’d. Illgner, Rauschenbach SS 2011 Current Topics in Distributed Multimedia Systems (2 VK + 2 PR) Böszörmenyi, AssistentIn WS 2010/11

Selected Topics in Computer and Network Architectures: Multimedia Interactive Applications: Creation, Delivery, and Visualization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to measure energy consumption for each component accurately and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming [1, 2, 3]. These components are classified into three categories [4]: (i) data centers, which include encoding, packaging, and storage on cloud data centers; (ii) networks, which include core network and access networks; and (iii) end-user devices which involve decoding, players, hardware, etc. In addition to identifying the primary components of video streaming that affect energy consumption, it is important to conduct a comprehensive analysis of the entire video streaming. It is also essential to balance energy optimization and service quality to ensure that energyefficient strategies are implemented without sacrificing the quality of video streaming services. This talk aims to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.

Energy Consumption in Video Streaming: Components, Measurements, and Strategies

Energy Consumption in Video Streaming: Components, Measurements, and Strategies

Energy Consumption in Video Streaming: Components, Measurements, and Strategies

Alpen-Adria-Universität

The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices. In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.

Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...

Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...

Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...

Alpen-Adria-Universität

Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video. As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance. This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning.

Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning

Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning

Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning

Alpen-Adria-Universität

Nowadays, HTTP Adaptive Streaming (HAS) has become the de-facto standard for delivering video over the Internet. More users have started generating and delivering high-quality live streams (usually 4K resolution) through popular online streaming platforms, resulting in a rise in live streaming traffic. Typically, the video contents are generated by streamers and watched by many audiences, geographically distributed in various locations far away from the streamers. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. This dissertation leverages edge computing capabilities and in-network intelligence to design, implement, and evaluate approaches to optimize Quality of Experience (QoE) and end-to-end (E2E) latency of live HAS. In addition, improving transcoding performance and optimizing the cost of running live HAS services and the network’s backhaul utilization are considered. Motivated by the mentioned issue, the dissertation proposes five contributions in two classes: optimizing resource utilization and light-weight transcoding.

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...

Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...

Alpen-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.

SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications

Alpen-Adria-Universität

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Energy Consumption in Video Streaming: Components, Measurements, and Strategies

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Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning

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Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...

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Semester Opening WS'10/'11

1. Semester Opening WS’10/’11 H. Hellwagner & Christian Timmerer Klagenfurt University (UNI-KLU)  Faculty of Technical Sciences (TEWI) Department of Information Technology (ITEC)  Multimedia Communication (MMC) http://www.uni-klu.ac.at/tewi/inf/itec/mmc/  https://www-itec.uni-klu.ac.at/mmc/blog/ Multimedia Communication + Computer and Network Architectures

2. Spezialisierungsfach „Rechner- und Netzarchitektur“ („Computer and Network Architectures“) Prof. Hellwagner Lehrveranstaltungen LV-Leiter LV-Termine Current Topics in Computer and Network Architectures: Multimedia Communication and Internet QoS (2 VK) Hellwagner WS 2010/11 Current Topics in Computer and Network Architectures: ... (2 VK) Hellwagner SS 2011 Seminar in Computer and Network Architectures (2 SE) Hellwagner SS 2011 Selected Topics in Computer and Network Architectures: Multimedia Interactive Applications: Creation, Delivery, and Visualization (2 VK) Concolato WS 2010/11 Selected Topics in Distributed Multimedia Systems: Systems for Multimedia Applications (2 VK) - To be conf’d. Illgner, Rauschenbach SS 2011 Current Topics in Distributed Multimedia Systems (2 VK + 2 PR) Böszörmenyi, AssistentIn WS 2010/11

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