ATHENA Christian Doppler (CD) Laboratory

IEEE International Conference on Communications (ICC)

May 16–20, 2022 | Seoul, South Korea

[PDF][Slides][Video]

Reza Farahani (Alpen-Adria-Universität Klagenfurt),  Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt), Christian Timmerer (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt).

Abstract: With the emerging demands of high-definition and low-latency video streams, HTTP Adaptive Streaming (HAS) is considered the principal video delivery technology over the Internet. Network-assisted video streaming schemes, which employ modern networking paradigms, e.g., Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing, have been introduced as promising complementary solutions in the HAS context to improve users’ Quality of Experience (QoE) as well as network utilization. However, the existing network-assisted HAS schemes have not fully used edge collaboration techniques and SDN capabilities for achieving the aforementioned aims. To bridge this gap, this paper introduces a coLlaborative Edge- and SDN-Assisted framework for HTTP aDaptive vidEo stReaming (LEADER). In LEADER, the SDN controller collects various information items and runs a central optimization model that minimizes the HAS clients’ serving time, subject to the network’s and edge servers’ resource constraints. Due to the NP-completeness and impractical overheads of the central optimization model, we propose an online distributed lightweight heuristic approach consisting of two phases that runs over the SDN controller and edge servers, respectively. We implement the proposed framework, conduct our experiments on a large-scale testbed including 250 HAS players, and compare its effectiveness with other strategies. The experimental results demonstrate that LEADER outperforms baseline schemes in terms of both users’ QoE and network utilization, by at least 22% and 13%, respectively.

Keywords:

Dynamic Adaptive Streaming over HTTP (DASH), Network-Assisted Video Streaming, Video Transcoding, Quality of Experience (QoE), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Edge Computing, Edge Collaboration.

Data Compression Conference (DCC)

March 22-25, 2022 | Snowbird, Utah, US

[PDF]

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract:

High Framerate (HFR) video streaming enhances the viewing experience and improves visual clarity. However, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, this paper proposes a content-aware frame dropping algorithm (CODA) to drop frames uniformly in every video (segment) according to the target bitrate and the video characteristics. The algorithm uses Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features to determine the video properties and then predict the optimized framerate, yielding the highest compression efficiency. The effectiveness of CODA is evaluated with High Efficiency Video Coding (HEVC) bitstreams based on the x265 HEVC open-source encoder. Experimental results show that, on average, CODA reduces the overall Ultra High Definition (UHD) encoding time by 21.82% with bit-rate savings of 15.87% and 18.20% to maintain the same PSNR and VMAF scores, respectively compared to the original frame-rate encoding.

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

Conference website

[PDF]

Farzad Tashtarian (AAU, Austria), R. Falanji (Sharif University of Technology), Abdelhak Bentaleb (National University of Singapore), Alireza Erfanian (AAU, Austria), P. S. Mashhadi (Halmstad University),
Christian Timmerer (AAU, Austria), Hermann Hellwagner (AAU, Austria), Roger Zimmermann (National University of Singapore)

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:

• Christian Timmerer, AAU, AT
• Tobias Hoßfeld, Univ. Würzburg, DE
• Raimund Schatz, AIT, AT

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).

Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain. [Slides]