Video streaming is one of the most popular Internet applications that makes up a large amount of Internet traffic. A fundamental mechanism in video streaming is adaptive bitrate (ABR) selection which decides the proper compression level for each chunk of a video to optimize the users’ quality of experience (QoE). The existing ABR algorithms require significant tuning and do not generalize to diverse network conditions and personalized QoE objectives. In this article, we propose a novel framework for meta-learning based ABR design and discuss challenges of deploying learning based ABR mechanism in real-world video streaming systems. We utilize the proposed framework to design MetaABR, a novel adaptive bitrate selection algorithm based on meta-reinforcement learning to maximize users’ QoE. By jointly training multiple learning tasks with a shared meta-critic, it can provide transferrable meta-knowledge to supervise bitrate selection across tasks, and can be applied to efficiently learn a new task in unseen environment with only a few trials. We implement MetaABR on an emulation platform which connects to the Linux network protocol stack through virtual network interfaces. Extensive experiments based on real-world traces and wireless testbed show that MetaABR achieves the best comprehensive QoE compared with the state-of-the-art ABR algorithms in a variety of network environments.

This paper proposes a new manifold learning method called "Soinnmanifold". Traditional manifold learning method needs a lot of computation and appropriate priori parameters. This has somewhat restricted the domains in which manifold learning can potentially be applied. However, with the high-dimensional inputs, our method can generate a lowdimensional manifold in the high-dimensional space and determine the intrinsic dimension automatically. Then we will use this manifold to do dimensionality reduction quickly. Experiments demonstrate that our method can get promising results with less time and memory.

Time series classification (TSC) problem is important due to the pervasiveness of time series data. Shapelet provides a mechanism for the problem by its ability to measure local shape similarity. However, shapelets need to be searched from massive sub-sequences. To address this problem, this paper proposes a novel shapelet learning method for time series classification. The proposed method uses a self-organizing incremental neural network to learn shapelet candidates. The learned candidates reduce greatly in quantity and improve much in quality. After that, an exponential function is proposed to transform the time series data. Besides, all shapelets are selected at the same time by using an alternative attribute selection technique. Experimental results demonstrate statistically significant improvement in terms of accuracies and running speeds against 10 baselines over 28 time series datasets.