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- W2807760262 abstract "Predictive green streaming have recently gained attention in wireless network literature due to its significant energy-savings and quality of experience (QoE) gains. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular Hypertext Transfer Protocol (HTTP) [e.g., HTTP adaptive streaming (HAS)]. To this end, we develop a stochastic predictive HTTP Adaptive streaming (PHAS) optimization framework to achieve the following objectives: 1) an edge-cloud assisted framework for prediction based HAS and identify its key functional entities and their interactions; 2) modelling uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach which dynamically allocate the risks and optimize system efficiency over a time horizon; 3) an efficient heuristic algorithm allocating time slot ratio for multi-users to improve the network efficiency, fairness and overall QoE under different prediction error variances, wireless link conditions and buffer length constraints; Simulation studies and analytical results show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency." @default.
- W2807760262 created "2018-06-21" @default.
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- W2807760262 date "2018-04-01" @default.
- W2807760262 modified "2023-09-23" @default.
- W2807760262 title "Energy-efficient predictive HTTP adaptive streaming in mobile cellular networks" @default.
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- W2807760262 doi "https://doi.org/10.1109/wcnc.2018.8377424" @default.
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