Category Archives: ATHENA

Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC

Posted on November 21, 2023 by

Klagenfurt, August 22, 2023 Congratulations to Dr. Reza Farahani for successfully defending his dissertation on “Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA. Abstract … Continue reading →

Posted in ATHENA | Comments Off on Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, SDN, and MEC

JPEG Image Encryption with DC Rotation and Undivided RSV-based AC Group Permutation

Posted on November 20, 2023 by

IEEE Transactions on Multimedia (TMM) [PDF] Yuan Yuan (Southwest Jiaotong University, China), Hongjie He (Southwest Jiaotong University, China), Yaolin Yang (Southwest Jiaotong University, China), Hadi Amirpour (AAU, Klagenfurt, Austria), Christian Timmerer (AAU, Klagenfurt, Austria), Fan Chen (Southwest Jiaotong University, China) Abstract: Existing … Continue reading →

Posted in ATHENA | Comments Off on JPEG Image Encryption with DC Rotation and Undivided RSV-based AC Group Permutation

ALIVE: A Latency- and Cost-Aware Hybrid P2P-CDN Framework for Live Video Streaming

Posted on November 16, 2023 by

IEEE Transactions on Network and Service Management  [PDF] Reza Farahani (Alpen-Adria-Universität Klagenfurt, Austria), Ekrem Çetinkaya (Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), Mohammad Shojafar (University of Surrey, UK), Mohammad Ghanbari (University of Essex, UK), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria) … Continue reading →

Posted in ATHENA | Comments Off on ALIVE: A Latency- and Cost-Aware Hybrid P2P-CDN Framework for Live Video Streaming

Towards Low-Latency and Energy-Efficient Hybrid P2P-CDN Live Video Streaming

Posted on November 16, 2023 by

Special Issue on Sustainable Multimedia Communications and Services, IEEE COMSOC MMTC Communications – Frontiers, [PDF] Reza Farahani(Alpen-Adria-Universität Klagenfurt, Austria), Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria), and Hermann Hellwagner (Alpen-Adria-Universität Klagenfurt, Austria) Abstract: Streaming segmented videos over the Hypertext Transfer Protocol (HTTP) … Continue reading →

Posted in ATHENA | Comments Off on Towards Low-Latency and Energy-Efficient Hybrid P2P-CDN Live Video Streaming

SIGMM Test of Time Paper Honorable Mention in the category of “MM Systems & Networking”

Posted on November 15, 2023 by

We’re excited to share that the ACM Special Interest Group in Multimedia (SIGMM) presents to Stefan Lederer, Christopher Müller, and Christian Timmerer The SIGMM Test of Time Paper Honorable Mention in the category of “MM Systems & Networking” for their … Continue reading →

Posted in ATHENA | Comments Off on SIGMM Test of Time Paper Honorable Mention in the category of “MM Systems & Networking”

Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Posted on October 18, 2023 by

The 19th International Conference on emerging Networking EXperiments and Technologies December 5-8, 2023 | Paris, France [PDF] [PPT] [PPT (Artifacts)] Leonardo Peroni (IMDEA Networks Institute and UC3M), Sergey Gorinsky (IMDEA Networks Institute), Farzad Tashtarian (AAU, Austria), and Christian Timmerer (AAU, Austria). … Continue reading →

Posted in ATHENA | Comments Off on Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality

Video Coding Enhancements for HTTP Adaptive Streaming using Machine Learning

Posted on October 3, 2023 by

Klagenfurt, June 7, 2023 Congratulations to Dr. Ekrem Çetinkaya for successfully defending his dissertation on “Video Coding Enhancements for HTTP Adaptive Streaming using Machine Learning” at Universität Klagenfurt in the context of the Christian Doppler Laboratory ATHENA. Abstract Video is … Continue reading →

Posted in ATHENA | Comments Off on Video Coding Enhancements for HTTP Adaptive Streaming using Machine Learning