Machine learning models, particularly reinforcement learning (RL), have demonstrated great
potential in optimizing video streaming applications. However, the state-of-the-art solutions …

As deep reinforcement learning (RL) showcases its strengths in networking, its pitfalls are
also coming to the public's attention. Training on a wide range of network environments …

Internet adaptive video streaming is a typical form of video delivery that leverages adaptive
bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for …

Maximizing the quality of experience (QoE) for real-time video is a long-standing challenge.
Traditional video transport protocols, represented by a few deterministic rules, can hardly …

Emerging applications raise critical QoS requirements for the Internet. The improvements of
flow classification technologies, software defined networks (SDN), and programmable …

Cloud platforms run many software agents on each server node. These agents manage all
aspects of node operation, and in some cases frequently collect data and make decisions …

Deep reinforcement learning (DRL) has shown good performance in tackling Markov
decision process (MDP) problems. As DRL opti-mizes a long-term reward, it is a promising …

Machine learning models, particularly reinforcement learning (RL), have demonstrated great
potential in optimizing video streaming applications. However, the state-of-the-art solutions …

The past few years have witnessed a surge of interest towards deep reinforcement learning
(DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the …

With the rapid development of mobile communication technology, there are an increasing
number of new network applications and services, and the existing best‐effort routing …