Ensemble Adaptive Streaming - A New Paradigm to Generate Streaming Algorithms via Specializations
Publication in refereed journal


摘要Video streaming is now ubiquitous in the mobile Internet. This motivated intense research in adaptive streaming algorithms to tackle mobile networks’ fluctuating conditions. Our investigations revealed that while existing algorithms can perform well in their intended operating environments, their performance can degrade substantially in other environments. This work tackles this challenge by developing a novel Ensemble Adaptive Streaming (EAS) paradigm to mobile video streaming. As opposed to designing a single streaming algorithm for all network conditions, we argue that different network conditions require different algorithms. We introduce the notion of network differentiators to segregate network conditions into different classes where each class has its own adaptation algorithm designed and optimized specifically for it. An EAS mobile streaming client then selects at runtime the matching adaptation algorithm using the same network differentiators on a per session basis for streaming. We show how EAS can be applied to existing machine-learning approaches to improve their performances. Moreover, to fully exploit EAS’s potential we developed the first Genetic Programming approach to evolve adaptive streaming algorithms. The resultant EAS-GP algorithms not only outperformed state-of-the-art algorithms substantially, but also exhibited remarkable robustness over time, location, mobile operators, as well as quality-of-experience metrics.
著者Guanghui Zhang, Jack Y. B. Lee
期刊名稱IEEE Transactions on Mobile Computing
出版地New York, NY, USA
頁次1346 - 1358
關鍵詞Video Streaming, Mobile Network, Genetic Programming, Quality-of-experience

上次更新時間 2020-31-07 於 23:27