In recent years, live streaming has become a popular application, which uses TCP as its primary transport protocol. Quick UDP Internet Connections (QUIC) protocol opens up new opportunities for live streaming. However, how to leverage QUIC to transmit live videos has not been studied yet. This paper first investigates the achievable quality of experience (QoE) of streaming live videos over TCP, QUIC, and their multipath extensions Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC). We observe that MPQUIC achieves the best performance with bandwidth aggregation and transmission reliability. However, network fluctuations may cause heterogeneous paths, high path loss, and bandwidth degradation, resulting in significant QoE deterioration. Motivated by the above observations, we investigate the multipath packet scheduling problem in live streaming and design 4D-MAP, a multipath adaptive packet scheduling scheme over QUIC. Specifically, a linear upper confidence bound (LinUCB)-based online learning algorithm, along with four novel scheduling mechanisms, i.e., Dispatch, Duplicate, Discard, and Decompensate, is proposed to conquer the above problems. 4D-MAP has been evaluated in both controlled emulation and real-world networks to make comparison with the state-of-the-art multipath transmission schemes. Experimental results reveal that 4D-MAP outperforms others in terms of improving the QoE of live streaming.

We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers (LADMM). Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM (AdaLADMM) algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.