Phuong L. Vo

Phuong L. Vo, Nguyen H. Tran, Choong Seon Hong

The concave utility in the Network Utility Maximization (NUM) problem is only suitable for elastic flows. However, the networks with the multiclass traffic, the utility of inelastic traffic is usually represented by the sigmoidal function which is a nonconcave function. Hence, the basic NUM problem becomes a nonconvex optimization problem. Solving the nonconvex NUM distributively is a difficult problem. The current works utilize the standard dual-based algorithm for the convex NUM and find the criteria for the global optimal convergence of the algorithm. It turns out that the link capacity must higher than a certain value to achieve the global optimum. We propose a new distributed algorithm that converges to the suboptimal solution of the nonconvex NUM for all of link capacity. We approximate the logarithm of the original problem to the convex problem which is solved efficiently by the standard dual-base distributed algorithm. After a sequence of approximations, the solutions converge to the KKT solution of the original problem. In many of our experiments, it also converges to the global optimal solution of the NUM. Moreover, we extend our work to solve the joint rate and power NUM problem with elastic and inelastic traffic in a wireless network. Our techniques can be applied to any log-concave utilities.

Nghia T. Nguyen, Long Luu, Phuong L. Vo et al.

Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user's quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, content-centric networks,... usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluating. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.