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Abstract
This paper presents an optimal and fair strategy for multiuser multimedia radio resource allocation (RRA) based on coopetition, which suggests a judicious mixture of competition and cooperation. We formulate the co-opetition strategy as sum utility maximization at constraints from both Physical (PHY) and Application (APP) layers. We show that the maximization can be solved efficiently employing the well-defined Layering as Optimization Decomposition (LOD) method. Moreover, the coopetition strategy is applied to power allocation among multiple video users, and evaluated through comparing with existing- competition based strategy. Numerical results indicate that, the co-opetition strategy adapts the best to the changes of network conditions, participating users, and so forth. It is also shown that the coopetition can lead to an improved number of satisfied users, and in the meanwhile provide more flexible tradeoff between system efficiency and fairness among users.
Highlights
Radio resource allocation (RRA) for multimedia services has drawn a lot of attention because of its capability of offering an efficient way to handle the resources
We focus on optimization-based strategy, that is, sum utility maximization (SUM)
Comparison in Terms of Individual Peak Signal-to-Noise Ratios (PSNRs). In this experiment we focus on individual PSNRs in the case of two users
Summary
Radio resource allocation (RRA) for multimedia services has drawn a lot of attention because of its capability of offering an efficient way to handle the resources. It is shown that the Nash Bargaining Solution (NBS), a welldefined notion in game theory, can be used to maximize the sum of Peak Signal-to-Noise Ratios (PSNRs) in rate allocation for collaborative video transmissions [1]. Optimal resource allocation for multiuser wireless transmissions is studied in [2] from an information theoretic perspective, and it is shown that sum rate maximization (SRM) is suboptimal when taking video quality into account. The maximization of weighted sum of data rates in cross-layer resource allocation is addressed in [8], and an improved conjugate gradient method under given power constraint is presented as well
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