Adaptive Bit Rate (ABR) decision plays a crucial role for ensuringsatisfactory Quality of Experience (QoE) in video streaming applications . Past network statistics are mainly leveraged for future network bandwidthprediction . This paper proposes to learn the ANT (a.k.a., Accurate Network Throughput) model to characterize the full spectrum of network throughput dynamics in the past forderiving the proper network condition associated with a specific cluster ofnetwork throughput segments (NTS) Each cluster of NTS is then used to generate a dedicated ABR model, by which we wish to better capture the network dynamicsfor diverse connections . Extensive experiment results show that our approach can significantlyimprove the user QoE by 65.5% and 31.3% respectively, compared with the state-of-the-art Pensive and Oboe, across a wide range of network scenarios, compared to the Pensive & Oboe

Author(s) : Jiaoyang Yin, Yiling Xu, Hao Chen, Yunfei Zhang, Steve Appleby, Zhan Ma

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : network - throughput - ant - accurate - nts -

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