On-demand video services such as Youtube and Hulu are expected to comprise a large percentage of the increasing data loads in mobile networks. On-demand video is distinctive because it is pre-recorded and therefore can be considered elastic traffic because the video frame buffer can be downloaded well past the current point of playback. Based on this observation, we propose Video Rest-and-Download (VR&D) as a video download application framework that aims to reduce network congestion while maintaining playback quality. The intuition for VR&D is that, in a scenario where radio resources are shared by multiple data users, the video user can “rest” for some amount of time until fewer users are in the network, thereby allowing other data users to complete their downloads faster, without affecting playback quality. We present an algorithmic framework for VR&D based on the Markov Decision Process that uses the history and current state of network activity to determine how aggressive the user should be in downloading video frames. We evaluate its performance using a simulated UMTS network with HSDPA data service based on real network traces from a major U.S. carrier. Our results show that, compared to the existing solution, during the time of video playback this application can reduce download time by as high as 50%, and alleviate network congestion by up to 30% with minimal effect on playback quality.

Cognitive radio (CR) improves spectrum efficiency by allowing secondary users (SUs) to dynamically exploit the idle spectrum owned by primary users (PUs). This paper studies optimal bandwidth allocation of SUs for throughput efficiency. Consider the following tradeoff: an SU increases its instantaneous throughput by accessing more spectrum, but channel access/switching overhead, contention among multiple SUs, and dynamic PU activity create higher liability for larger bandwidths. So how much is too much? In this paper, we study the optimal bandwidth allocation for multiple SUs. Our approach is twofold. We first study the optimal bandwidth an SU should use to maximize the per-SU throughput in the long term. The optimal bandwidth is derived in the context of dynamic PU activity, where we consider both independent and correlated PU channel scenarios while accounting for the effects of channel switching overhead. We further consider the case of suboptimal spectrum use by SUs in the short term due to PU activity dynamics. We propose an efficient channel reconfiguration (CREC) scheme to improve SUs' performance. We use real PU channel activity traces in the simulations to validate our results. The work sheds light on the design of spectrum sharing protocols in cognitive radio networks.

Cognitive radio (CR) technology has great potential to alleviate spectrum scarcity in wireless communications. It allows secondary users (SUs) to opportunistically access spectrum licensed by primary users (PUs) while protecting PU activity. The protection of the PUs is central to the adoption of this technology since no PU would accommodate SU access to its own detriment. In this paper, we consider an SU that must protect multiple PUs simultaneously. We focus on the PU packet collision probability as the protection metric. The PUs are unslotted and may have different idle/busy time distributions and protection requirements. Under general idle time distributions, we determine the form of the SU optimal access policy and identify two special cases for which the computation of the optimal policy is significantly reduced. We also present a simple algorithm to determine these policies using principles of convex optimization theory. We then derive the optimal policy for the same system when an SU has extra “side information” on PU activity. We evaluate the performance of these policies through simulation.