OPTIMIST - OPTIMised video content delivery chains leveraging data analysis over joint multI-accesS edge computing and 5G radio network infrasTructures

Ico_CTTC
Start: 01/05/2020
End: 30/04/2024
Funding: European
Status: On going

Acronym: OPTIMIST
Call ID: H2020-MSCA-RISE-2019
Code: 872866

The OPTIMIST programme sets the ambitious aim to develop a modular end-to-end service platform tailored to the optimized delivery of personalised video content in 5G mobile networks, providing one of the first world-wide implementations of MEC-enabled service provisioning in 5G networks that is fully compatible with the emerging ETSI/3GPP reference architectures. To achieve this, the OPTIMIST service platform will design and implement different MEC services, which are currently studied in an isolated fashion in current literature, (e.g. edge network caching exploiting edge storage resources, video transcoding and data-driven content popularity prediction exploiting edge processing resources, optimized video content placement and delivery exploiting the new 5G radio capabilities), in the form of virtual network functions (VNFs) that are instantiated and optimized on-the-fly to construct a video service chain that is designed to meet the personalized requirements of 5G mobile video consumers. To formalize the integration of MEC capabilities into the operation of 5G mobile networks, the OPTIMIST service platform will be fully aligned with the existing and forthcoming standardization activities in the areas of MEC and 5G networks (ETSI MEC and 3GPP); thus, maximizing the impact of the project in the long-term both in terms of academic results (new methodological tools and algorithmic innovations) and market products (deliver specific products / services that will enhance the portfolio of industrial partners). To this end, OPTIMIST will leverage state-of-the-art (SotA) technologies in MEC-empowered service provisioning, data-driven service control and automation, QoE-driven dynamic adaptive video streaming over HTTP (DASH), GPS-free localization and machine learning (ML) for wireless communications.

Christos Verykoukis
PI/Project Leader
Hatim Chergui
Researcher
Miquel Payaró
Researcher
David Pubill
Researcher
Jordi Serra
Researcher
Sarang Kahvazadeh
Researcher
P.C. FOGUS INNOVATIONS & SERVICES
No results found