Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.
The link to the presentation can be found here (pdf).
IEEE Global Communications Conference
December 07-11, 2021 | Madrid, Spain
[PDF]
Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7 x.
VCIP 2021
Sunday, December 5, 2021
https://www.vcip2021.org/call-for-tutorials/
Lectures:
Abstract: Universal access to and provisioning of multimedia content is now a reality. It is easy to generate, distribute, share, and consume any multimedia content, anywhere, anytime, on any device thanks to a plethora of applications and services that are now commodities in our daily life. Interestingly, most of these services adopt a streaming paradigm, are typically deployed over the open, unmanaged Internet, and account for most of today’s Internet traffic. Currently, the global video traffic is greater than 60 percent of all Internet traffic and it is expected that this share will grow to more than 80 percent in the near future (according to Sandvine and Cisco VNI). Additionally, Nielsen’s Law of Internet bandwidth states that the users’ bandwidth grows by 50 percent per year, which roughly fits data from 1983 to 2019. Thus, the users’ bandwidth can be expected to reach approximately 1 Gbps by 2022. At the same time, network applications will grow and utilize the bandwidth provided, just like programs and their data expand to fill the memory available in a computer system. Most of the available bandwidth today is consumed by video applications and the amount of data is further increasing due to already established and emerging applications, e.g., ultra-high definition, high dynamic range, or virtual, augmented, mixed realities, or immersive media applications in general with the aim to increase the Immersive Media Experience (IMEx).
A major technical breakthrough was the adaptive streaming over HTTP resulting in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (DASH), which enables a content-/format-agnostic delivery over-the-top (OTT) of the existing infrastructure. Thus, this tutorial takes DASH as a basis and explains how it is adopted for immersive media delivery such as omnidirectional/360-degree video and any other volumetric video representations (i.e., point clouds, light fields, holography). The focus of this tutorial is related to the principles of Quality of Experience (QoE) for such immersive media applications and services including its assessment and management. Finally, this tutorial concludes with open research issues and industry efforts in this domain.
The ANGELA: HTTP Adaptive Streaming and Edge Computing Simulator paper from ATHENA lab has won the 2nd Best Paper Award in the 10th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN).
More information about the paper can be found in the blog post.
Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain. [Slides]
At Christian Doppler laboratory ATHENA, we offer an internship*) for 2022 for Master Students and we kindly request your applications until 14th of December 2021 with the following data (in German or English):
*) A 3-month period in 2022 (with an exact time slot to be discussed) with the possibility to spend up to 1-month at the industrial partner; 20h per week “Universitäts-KV, Verwendungsgruppe C1, studentische Hilfskraft”
Please send your application by email to nina.stiller@aau.at.
About ATHENA: The Christian Doppler laboratory ATHENA (AdapTive Streaming over HTTP and Emerging Networked MultimediA Services) is jointly proposed by the Institute of Information Technology (ITEC; http://itec.aau.at) at Alpen-Adria-Universität Klagenfurt (AAU) and Bitmovin GmbH (https://bitmovin.com) to address current and future research and deployment challenges of HAS and emerging streaming methods. AAU (ITEC) has been working on adaptive video streaming for more than a decade, has a proven record of successful research projects and publications in the field, and has been actively contributing to MPEG standardization for many years, including MPEG-DASH; Bitmovin is a video streaming software company founded by ITEC researchers in 2013 and has developed highly successful, global R&D and sales activities and a world-wide customer base since then.
The aim of ATHENA is to research and develop novel paradigms, approaches, (prototype) tools, and evaluation results for the phases
The new approaches and insights are to enable Bitmovin to build innovative applications and services to account for the steadily increasing and changing multimedia traffic on the Internet.
Title: FSpot: Fast Multi-Objective Heuristic for Efficient Video Encoding Workloads over AWS EC2 Spot Instance Fleet [PDF] *** open access ***
Authors: Anatoliy Zabrovskiy, Prateek Agrawal, Vladislav Kashansky, Roland Kersche, Christian Timmerer, and Radu Prodan
Abstract: HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients’ budgets. Thus, there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations. Additionally, the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content. In this paper, we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple x264 codec encoding parameters and video sequences of varying complexity. Then, we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs. Furthermore, we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost. The results show that our approach, on average, can reduce the encoding costs by at least 15.8% and up to 47.8% when compared to a random selection of EC2 spot instances.
Keywords: EC2 Spot instance, Encoding time prediction; adaptive streaming; video transcoding; Clustering; HTTP adaptive streaming; MPEG-DASH; Cloud computing; optimization; Pareto front.
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