Software defined cognitive networking: supporting intelligent online video streaming

Mu, M. (2018). Software defined cognitive networking: supporting intelligent online video streaming. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). https://doi.org/10.1109/CCNC.2018.8319167

Abstract
Adaptive media such as HTTP adaptive streaming (HAS) is becoming a standard tool for online video distribution. The non-cooperative competition of network resources between a growing number of adaptive video applications has a significant detrimental impact on user experience and network efficiency. Existing network infrastructures often prioritise fast packet forwarding, which do not always contribute to the improved user experience. Future network management must leverage application and user-level cognitive factors to allocate scarce network resources effectively and intelligently. Our software defined cognitive networking (SDCN) project, supported by the Research Councils UK, aims at incorporating new developments in human cognition, media technology and communication networks to ensure the user experience, user-level fairness and network efficiency of online adaptive media using software defined networking-assisted and QoE-aware resource management.


To read goto: https://pure.northampton.ac.uk/en/publications/software-defined-cognitive-networking-supporting-intelligent-onli-2

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Bio-inspired streaming player

Sani, Y., Mu, M., Mauthe, A. and Edwards, C. (2016) A Bio-inspired HTTP-based adaptive streaming player. In: 2016 IEEE International Conference on Multimedia and Expo (ICME 2016). Seattle, USA: IEEE. (Accepted)


Abstract
In order to streamline video content distribution on a myriad of platforms over heterogeneous networks, HTTP Adaptive Streaming (HAS) has been increasingly adopted. In this paper we pilot a bio-inspired HAS optimisation design with the aim to maximise the overall use experiences of a video playback session. Evaluations conducted within a real-world Internet environment demonstrate the benefit of our design using quality indicators such as convergence time, start-up delay, average video rate, stability, and fairness.

To read more go to https://drmu.net/2016/04/30/paper-accepted-by-ieee-icme-grand-challenges/





If you'd like to find out more about Computing at the University of Northampton go to: www.computing.northampton.ac.uk. All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with