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.
Further benefits of our work are its generality and low computational complexity. Being based on bit rates, our buffer model captures arbitrary video and audio codecs, while being independent of subjective quality metrics. This level of tractability is not provided by the Utility-based formulation in @cite_1 . Finally, our buffer model enables to incorporate the dynamics of streaming media traffic into a Linear Program. Such low complexity is not provided by the combinatorial approach in @cite_7 .
@cite_1 @cite_7
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Wired and wireless data networks have witnessed a rapid proliferation of multimedia applications such as Internet video streaming, video conferencing, etc. A desirable key feature for multimedia transmission over multiuser environments is the ability of adapting rate and quality of video stream to different QoS conditions. The most efficient approach to address the scalability of multimedia applications is to encode video streams in compliance with the scalable video coding (SVC) standard, which is proposed as an extension to H.264 AVC standard. This paper addresses the rate control and bandwidth sharing for multimedia applications that are relying on SVC-encoded video streams. In previous studies, idealistic utility functions, mainly in the form of a staircase function, were used to cast the rate allocation for SVC-encoded streams as an optimization problem. These utility functions make the optimization problem non-convex and even non-differentiable, for which achieving optimality through dual-based approaches proves quite challenging. Towards this goal, we introduce an accurate analytical model of the utility function for SVC-encoded video streams. Using the abovementioned utility model and adopting the utility-proportional optimization approach, we come up with a convex formulation and propose a dual-based distributed algorithm for rate allocation of SVC-encoded streams. To the best of our knowledge, this is the first work that focuses on an accurate utility function modeling for SVC-encoded streams. Simulation experiments show that the proposed algorithm is quite efficient in achieving the convergence towards the global optimality. We study the optimal streaming of variable bit-rate (VBR) video over a random VBR channel. The goal of a streaming application is to enable the successful decoding of each video object before its displaying deadline is violated. Hence, we define the main performance metric of a streaming system as the probability of un-interrupted video presentation, or jitter-free probability. Previous literature has described solutions to estimate the jitter-free probability by assuming either independence in the encoded data process or simplistic channel models. In this work, we present a novel analytical framework, which requires only some basic statistical information of an arbitrary VBR channel, to bound the probability of jitter-free playout under the constraint of initial playout delay and receiver buffer size. Both the infinite and finite buffer cases are considered. This technique is then applied to investigate streaming over a wireless system modeled by an extended Gilbert channel with ARQ transmission control. Experimental results with MPEG-4 VBR encoded video demonstrates that the proposed analysis derives close bounds to the actual system performance. Finally, we show that the proposed analysis provides a theoretical foundation to quantify the tradeoffs between the initial playout delay, the receiver buffer size, and the jitter-free probability for a general class of VBR streaming over random VBR channels. Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1 V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission. Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1 V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and BOLA is part of the reference player implementation for the evolving DASH standard for video transmission.