Quality-Aware Video Streaming for Green Cellular Networks With Hybrid Energy Sources

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Automated summary

The explosive growth of mobile video traffic in recent years has led to significant energy expenditure for mobile network operators. To reduce this expenditure, researchers propose leveraging renewable energy sources for cellular traffic delivery in green cellular networks. By formulating a stochastic optimization problem, they aim to maximize network service utility by considering factors such as grid electricity price, energy harvesting, and rate adaptation. Through extensive simulations, the proposed algorithm proves to be efficient in optimizing video quality and energy expenditure.