User dissatisfaction due to buffering pause during streaming is a significant cost to the system, which we model as a non-decreasing function of the frequency of buffering pause. Minimization of the total user dissatisfaction in a multi-channel cellular network leads to a non-convex problem. By utilizing a combinatorial structure in the problem, we first propose a polynomial time joint admission control and channel allocation algorithm which is provably (almost) optimal. This scheme assumes that the base station (BS) knows the multimedia frame statistics of the streams. In a more practical setting, where these statistics are not available a priori at the BS, a learning based scheme with provable guarantees is developed. This learning based scheme is related to regret minimization in multi-armed bandits with non-i.i.d. and delayed reward (cost). All these algorithms require none to minimal feedback from the user equipment to the base station regarding the states of the media player buffer at the application layer, and hence, are of practical interest.

中文翻译：

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用于平滑流式传输的多通道资源分配：非凸性和强盗


由于流媒体期间的缓冲暂停而导致的用户不满意对系统来说是一个重大成本，我们将其建模为缓冲暂停频率的非递减函数。多信道蜂窝网络中总用户不满的最小化会导致非凸问题。通过在问题中利用组合结构，我们首先提出了一种可证明（几乎）最优的多项式时间联合准入控制和信道分配算法。该方案假设基站（BS）知道流的多媒体帧统计数据。在更实际的环境中，BS 无法先验获得这些统计数据，因此开发了一种具有可证明保证的基于学习的方案。这种基于学习的方案与具有非独立同分布和延迟奖励（成本）的多臂老虎机的遗憾最小化有关。所有这些算法都不需要或最少地需要从用户设备到基站的关于应用层的媒体播放器缓冲器的状态的反馈，因此具有实际意义。