| Title | Intelligent Video Platform “APOLLO” |
|---|---|
| Period | October 2021 – September 2022 |
| Description | The main objective of APOLLO is to research and develop an intelligent video platform for HTTP adap-tive streaming which • shall provide means for the intelligent distribution of video processing tasks (primarily video transcoding) within current and future distributed environments for efficient HTTP adaptive streaming; • shall support manifold cloud-like environments ranging from large-scale data centers to small-scale computing environments possibly distributed within the delivery network; • shall integrate artificial intelligence (AI) / machine learning (ML) techniques for the actual distribution; • shall support media applications specifically in – but not limited to – the context of 5G, IoT, automotive, industry 4.0, as well as health care and education; • shall provide real-time intelligent metrics and indicators for the automated efficient resource management. The idea is to design a cloud-based intelligent video platform that also includes algorithms and tools for Quality of Experience (QoE)-efficient resource management. In this way, the client/customers will be able to get resources with required/predefined Quality of Service (QoS)/QoE values/classes. A mod-ern cloud resource management system should manage cloud encoding/transcoding operations from a QoE perspective, built with a future-proof core architecture and natively configured to use artificial intelligent/machine learning (AI/ML) models; The expected outcome of the APOLLO project is an intelligent video platform for video processing/en-coding that is efficient and cost-effective on current and future computing platforms with the following novel features: • elastic and scalable platform with an extensible modular architecture for delivering profes-sional-grade video processing/encoding at scale; • significant resources saving as well as video quality improvements; • programmable platform with a comprehensive suite of tools and APIs; • support for various input/output formats including support for latency-critical applications; • support for various quality of experience metrics. |
| Project leader | Assoc.-Prof. DI Dr. Christian Timmerer |
| christian.timmerer@aau.at | |
| Employees | Dr. Samira Afzal; Hamid Hadian MSc |
| Cooperations | Bitmovin GmbH |
| Sponsored or supported by | FFG |