IEEE MMTC Interest Group on AI in Multimedia Communications and Applications


We are very pleased to welcome you to the IEEE Multimedia Communication Technical Committee (MMTC) Interest Group on AI in Multimedia Communications and Applications (AImcA).

IEEE MMTC has more than 1000 active members worldwide who are researchers and engineers in multimedia communications. It has been expanding rapidly, and we expect to see even faster growth soon. MMTC represents IEEE ComSoc in many high profile technical activities, such as supporting IEEE Transactions and journals (for example, TMM, JSAC, IEEE Communications, IEEE Multimedia) as well as IEEE conferences (GLOBECOM, ICC, and ICME).

Recent progresses on machine learning, especially deep learning and reinforcement learning, open an exciting new era of knowledge-based multimedia communications. The AImcA of MMTC strives to promote the interdisciplinary research of artificial intelligence and multimedia communications.

This websites provides a platform to share knowledge and expertise, a forum for discussion and the means for productive networking. We wish to work closely with you and make AImcA a nice home for researchers and practitioners worldwide in this area!


AImcA aims to provide a platform for researches and practitioners from academia and industry to exchange research ideas and cross-pollinate, with a focus on emerging AI methods (e.g., deep reinforcement learning, generative adversarial networks, deep transfer learning) and killer multimedia applications (e.g., HDTV, 3DTV, IoT). It will assist the IEEE MMC by organizing and supporting ComSoc-sponsored conferences and workshops, and special issues of relevant journals. The technical topics of AImcA will span, but not limited to the following:

  • Machine learning, data mining and big data analytics in multimedia communications
  • Learning based automated and closed-loop multimedia networking optimization
  • Self-Learning for adaptive multimedia networking protocols and algorithms
  • Resource allocation for multimedia applications using machine learning
  • Deep learning and reinforcement learning in multimedia networking control and management
  • Predictive-aware multimedia networking maintenance and optimization
  • Deep learning based multimedia distribution and transmission
  • Proactive multimedia network monitoring for security and diagnosis
  • User experience-driven virtual or augmented reality (VR/AR) applications
  • Real-time methods and applications for multimedia applications, such as cloud gaming and remote surgery