Self-optimised video streaming

  • Enable on-demand or real-time video streaming at large scale for commercial and consumer video applications.
  • Provide a novel autonomous control loop for automatic monitoring of the metadata of the video flows and network conditions to adapt the video flows in response to the dynamic network conditions to avoid any degraded perceived quality by the end users even in adverse network conditions e.g., congested link.
  • Enable self-optimisation of video delivery in adverse network conditions with zero human intervention based on cognitive control loop to maintain the Quality of Experience (QoE) in 5G and beyond network for the end users in a highly stressed situation.
  • Enable latest video codec (H.265) to be employed.
  • Applicable to various media use cases such as UHD mobile TV, online gaming, on-demand UHD video streaming, and other media-rich applications.
  • Deployed in Aveiro, Portugal, in collaboration with Portugal Telecom in the EU 5G SELFNET project.

As shown in the demonstration video, a 4K UHD video was streaming smoothly from a server to a client over a 4G/5G network in a normal network condition before the congestion took place in the data path of the video. This resulted in a downgrade of the video quality. Meanwhile, the UWS autonomous control loop detected this situation and reacted promptly by triggering actuation to restore the QoE for the client.


P. Salva, J. M. Alcaraz Calero, Q. Wang, and J. B. Bernabe, ” Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks,” IEEE Transactions on Network and Service Management (TNSM), accepted.

I. Irondi, Q. Wang, C. Grecos, J. M. Alcaraz Calero, and P. Casaseca, “Efficient QoE-Aware Scheme for Video Quality Switching Operations in Dynamic Adaptive Streaming”, ACM Transactions on Multimedia Computing, Communications, and Applications,Vol. 15, No. 1, Feb 2019. (doi: 10.1145/3269494) (Free Full Text Downloading)

P. Salva-Garcia, J. Alcaraz Calero, R. Marco Alaez, E. Chirivella-Perez, J. Nightingale and Q. Wang, “5G-UHD: Design, Prototyping and Empirical Evaluation of Adaptive Ultra-High-Definition Video Streaming Based on Scalable H.265 in Virtualised 5G Networks”, (Elsevier) Computer Communications, Vol. 118, Mar 2018, pp. 171-184. (doi: 10.1016/j.comcom.2017.11.007)(Free Full Text Downloading)

J. Nightingale, P. Salva-Garcia, J. M. Alcaraz Calero, and Q. Wang, “5G-QoE: QoE Modelling for Ultra-HD Video Streaming in 5G Networks”, IEEE Transactions on Broadcasting, Vol. 64, No. 2, June 2018, pp. 621-634. (doi: 10.1109/TBC.2018.2816786)(Free Full Text Downloading)

J. Nightingale, Q. Wang, J. M. Alcaraz Calero, E. Chirivella-Perez, M. Ulbricht, A. Foglar, J. Alonso, R. Preto, T. Batista, T. Teixeira, M. J. Barros and C. Reinsch, “QoE-Driven, Energy-Aware Video Adaptation in 5G Networks: The SELFNET Self-Optimisation Use Case”, Special Issue on Advances on Software Defined Sensor, Mobile, and Fixed Networks, (Hindawi)International Journal of Distributed Sensor Networks, Vol. 2016, 2016, Article ID 7829305, 15 pages. (doi: 10.1155/2016/7829305 for free full text downloading too)