This paper describes a new approach for allocating resources to video streaming traffic. Assuming that the future channel state can be predicted for a certain time, we minimize the fraction of the bandwidth consumed for smooth streaming by jointly allocating wireless channel resources and play-out buffer size. To formalize this idea, we introduce a new model to capture the dynamic of a video streaming buffer and the allocated spectrum in an optimization problem. The result is a Linear Program that allows to trade off buffer size and allocated bandwidth. Based on this tractable model, our simulation results show that anticipating poor channel states and pre-loading the buffer accordingly allows to serve more users at perfect video quality.
@cite_5: This paper introduces the novel concept of proactive resource allocation for wireless networks, through which the predictability of user behavior is exploited to balance the wireless traffic over time, and significantly reduces the bandwidth required to achieve a given blocking outage probability. We start with a simple model in which smart wireless devices are assumed to predict the arrival of new requests and submit them to the network time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain to establish that the decay rate of the outage event probabilities increases with the prediction duration . Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed light on multicasting with predictable demands and show that proactive multicast networks can achieve a significantly higher diversity gain that scales superlinearly with . Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive wireless resource framework.
@cite_1: Multi-user video streaming over wireless channels is a challenging problem, where the demand for better video quality and small transmission delays needs to be reconciled with the limited and often time-varying communication resources. This paper presents a framework for joint network optimization, source adaptation, and deadline-driven scheduling for multi-user video streaming over wireless networks. We develop a joint adaptation, resource allocation and scheduling (JARS) algorithm, which allocates the communication resource based on the video users' quality of service, adapts video sources based on smart summarization, and schedules the transmissions to meet the frame delivery deadlines. The proposed algorithm leads to near full utilization of the network resources and satisfies the delivery deadlines for all video frames. Substantial performance improvements are achieved compared with heuristic schemes that do not take the interactions between multiple users into consideration.
@cite_7: Today's video delivery solutions for mobile terminals often use the HTTP Live Streaming (HLS) protocol, which has two interesting features: firstly, the video is divided into playable segments of a certain length, which allows to download and buffer segments before they are required for playback. And secondly, those segments can be available in different quality levels, which can be selected according to the available transmission capacity. Combining these features with scheduling and channel capacity prediction of wireless transmissions into a cross-layer scheduling approach could improve the QoE for users by reducing playback interruptions and by providing the best possible video quality depending on the user's wireless channel capacity. In this paper we propose a mixed integer quadratically constrained program (MIQCP) to implement the aforementioned cross-layer scheduling approach. We evaluate its performance compared to greedy strategies to assess the potential for a future polynomial time implementation.
@cite_2: 
Compared to this work, our approach differs two-fold. First, it does not adapt the video quality but adjusts the size of the video play-out buffer along with the wireless data rate. Unlike @cite_7 and @cite_1 , this enables to trade off buffer size with the amount of allocated resources. Second, our adaptation scheme is . Unlike any of the above approaches, our joint buffer-rate allocation is based on a prediction of the user's average channel gain. Using this prediction enables our scheme to plan its resource allocation ahead. This enables to compensate for upcoming channel outages (e.g., when a user drives through a tunnel) by pre-loading the video buffer in advance. Although this idea of anticipation has been applied for software interfaces @cite_2 and cognitive radios @cite_5 , it has not been proposed for media streaming so far.