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_0: We present a high-performance prefetching protocol for the delivery of prerecorded VBR video from a server across a packet-switched network to a large number of clients. Not only does the protocol give constant perceptual quality and almost 100 link utilization, but it also allows for immediate commencement of the video upon user request and near instantaneous response to pause resume and temporal jumps. The protocol requires: (1) that each client have a small amount of memory dedicated to the application; (2) that there is one bottleneck shared link between the server and the clients. Our protocol is based on the observation that there are frequent periods of time during which the shared link's bandwidth is under utilized. During these periods the server can prefetch frames from any of the ongoing videos and can send the frames to the buffers in the appropriate clients. The server chooses prefetched frames according to a join-the-shortest-queue policy. We present simulation results of our prefetch policy that are based on MPEG encoded traces.
@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_3: Video adaptation is an emerging field that offers a rich body of techniques for answering challenging questions in pervasive media applications. It transforms the input video(s) to an output in video or augmented multimedia form by utilizing manipulations at multiple levels (signal, structural, or semantic) in order to meet diverse resource constraints and user preferences while optimizing the overall utility of the video. There has been a vast amount of activity in research and standard development in this area. This paper first presents a general framework that defines the fundamental entities, important concepts (i.e., adaptation, resource, and utility), and formulation of video adaptation as constrained optimization problems. A taxonomy is used to classify different types of adaptation techniques. The state of the art in several active research areas is reviewed with open challenging issues identified. Finally, support of video adaptation from related international standards is discussed.
There is a large body of work on techniques for adaptive video streaming. At the application layer, channel-aware prefetching schemes invoke a traffic burst at high channel gain @cite_3 @cite_0 . At the link layer, cross-layer schedulers to jointly adapt video quality and wireless resource allocation have been proposed @cite_7 @cite_1 .