PROJECT TITLE :

Optimal Rate Allocation for Video Streaming in Wireless Networks With User Dynamics

ABSTRACT:

We have a tendency to think about the problem of optimal rate allocation and admission management for adaptive video streaming sessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the amount of users supported. We tend to derive sample path upper bounds for the long-term web utility rate in terms of either a linear program or a concave optimization problem, depending on whether or not the admissible rate set is discrete or continuous. We tend to then show that the higher bounds are asymptotically achievable in massive-scale systems by policies that either deny access to a user or assign it a mounted rate for its entire session, without wishing on any advance knowledge of the period. Moreover, the asymptotically optimal policies exhibit a specific structure, that enable them to be characterized through just one variable, and have the additional property that the induced offered load is unity. We exploit the latter insights to plan parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. In depth simulation experiments demonstrate that the proposed algorithms perform well, even in comparatively small-scale systems.