This paper aims to reduce the prebuffering requirements, while maintaining continuity, for video streaming. Current approaches do this by making use of adaptive media playout (AMP) to reduce the playout rate. However, this introduces playout distortion to the viewers and increases the viewing latency. We approach this by proposing a frame rate optimization framework that adjusts both the encoder frame generation rate and the decoder playout frame rate. Firstly, we model this problem as the joint adjustment of the encoder frame generation interval and the decoder playout frame interval. This model is used with a discontinuity penalty virtual buffer to track the accumulated difference between the receiving frame interval and the playout frame interval. We then apply Lyapunov optimization to the model to systematically derive a pair of decoupled optimization policies. We show that the occupancy of the discontinuity penalty virtual buffer is correlated to the video discontinuity and that this framework produces a very low playout distortion in addition to a significant reduction in the prebuffering requirements compared to existing approaches. Secondly, we introduced a delay constraint into the framework by using a delay accumulator virtual buffer. Simulation results show that the the delay constrained framework provides a superior tradeoff between the video quality and the delay introduced compared to the existing approach. Finally, we analyzed the impact of delayed feedback between the receiver and the sender on the optimization policies. We show that the delayed feedbacks have a minimal impact on the optimization policies.
AMP has been studied extensively for audio applications, @cite_15 @cite_35 @cite_31 @cite_7 @cite_11 @cite_23 . These audio AMP techniques depend on audio scaling techniques such as WSOLA @cite_33 , which allows audio to be scaled without changing its pitch. A recent work by has shown that audio and video scaling can be done in real-time @cite_41 . In this paper, we focus on AMP for video.
@cite_35 @cite_31 @cite_33 @cite_7 @cite_41 @cite_23 @cite_15 @cite_11
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We consider compressive imaging problems, where images are reconstructed from a reduced number of linear measurements. Our objective is to improve over existing compressive imaging algorithms in terms of both reconstruction error and runtime. To pursue our objective, we propose compressive imaging algorithms that employ the approximate message passing (AMP) framework. AMP is an iterative signal reconstruction algorithm that performs scalar denoising at each iteration; in order for AMP to reconstruct the original input signal well, a good denoiser must be used. We apply two wavelet-based image denoisers within AMP. The first denoiser is the “amplitude-scale-invariant Bayes estimator” (ABE), and the second is an adaptive Wiener filter; we call our AMP-based algorithms for compressive imaging AMP-ABE and AMP-Wiener. Numerical results show that both AMP-ABE and AMP-Wiener significantly improve over the state of the art in terms of runtime. In terms of reconstruction quality, AMP-Wiener offers lower mean-square error (MSE) than existing compressive imaging algorithms. In contrast, AMP-ABE has higher MSE, because ABE does not denoise as well as the adaptive Wiener filter. Dynamic voltage and frequency scaling (DVFS) has been a useful power management strategy in embedded systems, mobile devices, and wireless sensor networks. Recently, it has also been proposed for servers and data centers in conjunction with service consolidation and optimal resource-pool sizing. In this paper, we experimentally investigate the scope and usefulness of DVFS in a server environment. We set up a multimedia server which will be used in two different scenarios. In the first scenario, the server will host requests to download video files of known and available formats. In the second scenario, videos of unavailable formats can be accepted; in which case the server employs a trans coder to convert between AVI, MPEG and SLV formats before the videos are downloaded. The workload we generate has a uniform arrival rate and an exponentially distributed video size. We use four dynamic scaling policies which are widely used with existing mainstream Linux operating systems. Our observation is that while the gain of DVFS is clear in the first scenario (in which a predominantly IO-bound application is used), its use in the second scenario is rather counterproductive. This paper presents a novel run-time dynamic voltage scaling scheme for low-power real-time systems. It employs software feedback control of supply voltage, which is applicable to off-the-shelf processors. It avoids interface problems from variable clock frequency. It provides efficient power reduction by fully exploiting slack time arising from workload variation. Using software analysis environment, the proposed scheme is shown to achieve 80 94 power reduction for typical real-time multimedia applications. In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth.