ATHENA Christian Doppler (CD) Laboratory

The 13th ACM Multimedia Systems Conference MMSys’22

14th – 17th June 2022 | Athlone, Ireland.

Conference Website

The ACM Multimedia Systems Conference (MMSys) provides a forum for researchers to present and share their latest research findings in multimedia systems. While research about specific aspects of multimedia systems is regularly published in the various proceedings and transactions of the networking, operating systems, real-time systems, databases, mobile computing, distributed systems, computer vision, and middleware communities, MMSys aims to cut across these domains in the context of multimedia data types.

This year, MMSys hosted around 150 on-site participants from academia and industry. Five ATHENA members traveled to Athlone, Ireland, to present four papers by Reza Farahani, Babak Taraghi, and Vignesh V Menon in two tracks, i.e., Open Dataset & Software Track and Demo & Industry Track.

• Multi-Codec Ultra High Definition 8K MPEG-DASH Dataset
• VCD: Video Complexity Dataset
• VCA: Video Complexity Analyzer
• Towards Low Latency Live Streaming: Challenges in a Real-World Deployment

Moreover, two presentations in Mentoring & Postdoc Networking event by ATHENA postdocs:

• Hadi Amirpour,  “Video Encoding Optimization for Live Video Streaming” (pdf)

• Farzad Tashtarian, “QoE Optimization in Live Streaming” (pdf)

We will present the poster “The Power in Your Pocket: Boosting Video Quality with Super-Resolution on Mobile Devices” at the Austrian Computer Science Day 2022 conference. The poster will summarize our research about improving the visual quality of video streaming on mobile devices by utilizing deep neural network-based enhancement techniques.

Join us at the Institute of Science and Technology Austria (ISTA) in Vienna on June 21.

Here is the list of papers that we cover in the poster:

1. Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streaming for Mobile Devices

2. MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks

3. LiDeR: Lightweight Dense Residual Network for Video Super-Resolution on Mobile Devices

We cordially invite you to join the  Long Night of Research / Lange Nacht der Forschung at the University of Klagenfurt on May 20, 2022, 4pm-11pm.

ATHENA will be present with two booths as follows.

#1: L70: How does video streaming work! (Lakeside Park, B12b.1.1)

More than 60% of the internet data volume is video content consumed via streaming services like YouTube, Netflix, or Flimmit. We show how video streaming services work. It is essential that the quality of the videos is played back as optimally as possible on various end devices. With us, you can gain practical experience and get to know differences in the quality of perception. In particular, we will show an animation video of how video streaming actually works, and visitors will be able to experience videos of different qualities and run some exciting experiments.

#2: U25: How to make video streaming faster and better! (Main University Building; jointly with Bitmovin)

Bitmovin, founded by graduates and employees of the University of Klagenfurt, is a global leader in online video technology. The aim is to develop new technologies that will improve the video streaming experience in the future, for example, through smooth image quality. We will show you the latest achievements from ATHENA and how video streaming can become even more innovative in the future.

Video Complexity Analyzer (VCA) and Video Complexity Dataset (VCD) were presented by Hadi Amirpour and Vignesh Menon in the NORM session of the VQEG meeting on 11 May 2022, in Rennes, France.

The presentation video of VCA is available here.

Optimizing QoE in Live Streaming over Wireless Networks using Machine Learning Techniques

CHIST-ERA Conference 2022 [Website][Abstract]

May 25, 2022, Edinburgh, UK

Empowered by today’s rich tools for media generation and collaborative production and the convenient wireless access (e.g., WiFi and cellular networks) to the Internet, crowdsourced live streaming over wireless networks have become very popular. However, crowdsourced wireless live streaming presents unique video delivery challenges that make a difficult tradeoff among three core factors: bandwidth, computation/storage, and latency. However, the resources available for these non-professional live streamers (e.g., radio channel and bandwidth) are limited and unstable, which potentially impairs the streaming quality and viewers’ experience. Moreover, the diverse live interactions among the live streamers and viewers can further worsen the problem. Leveraging recent technologies like Software-defined Networking (SDN), Network Function Virtualization (NFV), Mobile Edge Computing (MEC), and 5G facilitate providing crowdsourced live streaming applications for mobile users in wireless networks. However, there are still some open issues to be addressed. One of the most critical problems is how to allocate an optimal amount of resources in terms of bandwidth, computation power, and storage to meet the required latency while increasing the perceived QoE by the end-users. Due to the NP-complete nature of this problem, machine learning techniques have been employed to optimize various items on the streaming delivery paths from the streamers to the end-users joining the network through wireless links. Furthermore, to tackle the scalability issue, we need to push forward our solutions toward distributed machine learning techniques. In this short talk, we are going first to introduce the main issues and challenges of the current crowdsourced live streaming system over wireless networks and then highlight the opportunities of leveraging machine learning techniques in different parts of the video streaming path ranging from encoder and packaging algorithms at the streamers to the radio channel allocation module at the viewers, to enhance the overall QoE with a reasonable resource cost.

Elsevier Computer Communications Journal

[PDF]

Shirzad Shahryari (Ferdowsi University of Mashhad, Mashhad, Iran), Farzad Tashtarian  (AAU, Austria), Seyed-Amin Hosseini-Seno (Ferdowsi University of Mashhad, Mashhad, Iran).

Abstract: Edge Cloud Computing (ECC) is a new approach for bringing Mobile Cloud Computing (MCC) services closer to mobile users in order to facilitate the complicated application execution on resource-constrained mobile devices. The main objective of the ECC solution with the cloudlet approach is mitigating the latency and augmenting the available bandwidth. This is basically done by deploying servers (a.k.a ”cloudlets”) close to the user’s device on the edge of the cellular network. Once the user requests mount, the resource constraints in a cloudlet will lead to resource shortages. This challenge, however, can be overcome using a network of cloudlets for sharing their resources. On the other hand, when considering the users’ mobility along with the limited resource of the cloudlets serving them, the user-cloudlet communication may need to go through multiple hops, which may seriously affect the communication delay between them and the quality of services (QoS).

EUVIP 2022 Special Session on

September, 2022, Lisbon, Portugal

Link

Organizers:

• Hadi Amirpour, University Klagenfurt, Austria
• Christine Guillemot, INSA, France
• Christian Timmerer, University Klagenfurt, Austria

Brief description:

The importance of remote communication is becoming more and more important in particular after  COVID-19 crisis. However, to bring a more realistic visual experience, more than the traditional two-dimensional (2D) interfaces we know today is required. Immersive media such as 360-degree, light fields, point cloud, ultra-high-definition, high dynamic range, etc. can fill this gap. These modalities, however, face several challenges from capture to display. Learning-based solutions show great promise and significant performance in improving traditional solutions in addressing the challenges. In this special session, we will focus on research works aimed at extending and improving the use of learning-based architectures for immersive imaging technologies.

Important dates:

Paper Submissions: 6^th June, 2022
Paper Notifications: 11^th July, 2022