Dynamic Resource Allocation and Layer Selection for Scalable Video Streaming in Femtocell Networks: A Twin-Time-Scale Approach

Abstract

Scalable video streaming over femtocell networks relying on two-tier spectrum-sharing is designed for coping with time-varying channel conditions, stringent video QoS requirements as well as with strong cross-tier interference between the over-sailing macro- and the femtocells. Dynamic video layer selection and resource allocation are invoked to enable the adaptation of the scalable video streaming service to the dynamics of both channel quality and interference price fluctuations. We formulate the design as a constrained stochastic optimization problem, which strikes a compelling compromise between the perceivable quality of experience and the monetary implications of the interference. Since the time scale of resource allocation is more short term than that of the video layer selection, we decompose the original long-term utility optimization problem into a pair of readily tractable subproblems with the aid of two different time-scales by invoking the powerful technique of Lyapunov drift and optimization. By exploiting the specific structure of these subproblems, low-complexity algorithms are derived for dynamic video layer selection and resource allocation, which rely on the near-instantaneously available information rather than on any prior statistical knowledge. Finally, we derive the analytical bounds of the theoretically achievable performance. Experimental results are presented for characterizing the performance attained.