With the COVID19 pandemic, the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. Through this project, adaptive rate control method was proposed using reinforcement learning technique to control the live video streaming. Experiment results proved that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. Another strategy was proposed based on Ising machine by using the quadratic unconstrained binary optimization (QUBO) method. Experiment results show that the proposed QUBO-based method outperforms the existing methods. To improve throughput and mitigate pilot contamination, an annealing-based pilot allocation method was proposed. Experiment results show that the proposed method can realize optimal pilot allocation and mitigate pilot contamination with higher minimum achievable rate and SINR, especially when the numbers of users and cells are large.