Article

Cross-Layer Optimization for Streaming Scalable Video over Fading Wireless Networks

IEEE Journal on Selected Areas in Communications (Impact Factor: 3.45). 05/2010; 28(3):344 - 353. DOI: 10.1109/JSAC.2010.100406
Source: IEEE Xplore

ABSTRACT

We present a cross-layer design of transmitting scalable video streams from a base station to multiple clients over a shared fading wireless network by jointly considering the application layer information and the wireless channel conditions. We first design a long-term resource allocation algorithm that determines the optimal wireless scheduling policy in order to maximize the weighted sum of average video quality of all streams. We prove that our algorithm achieves the global optimum even though the problem is not concave in the parameter space. We then devise two on-line scheduling algorithms that utilize the results obtained by the long-term resource allocation algorithm for user and packet scheduling as well as video frame dropping strategy. We compare our schemes with existing video scheduling and buffer management schemes in the literature and simulation results show our proposed schemes significantly outperform existing ones.

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Available from: Mohammad Khojastepour, Jan 05, 2015
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    • "Despite being attractive in terms of system throughput, MDCM cannot guarantee the successful reception of key information and hence applies poorly in practice. By comparison, in LVM [18] [19], the video data is encoded into a base layer (BL) and several enhancement layers (ELs). The BL is intended to all subscribers at a low rate and hence can guarantee a basic recovered video quality, while the ELs are transmitted at incremental rates and opportunistically received by subscribers with promising channel conditions to persistently improve the video quality. "
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    ABSTRACT: An energy-efficient layered video multicast (LVM) scheme for “ bandwidth-hungry ” video services is studied in OFDM-based cognitive radio (CR) systems, where the video data is encoded into a base layer and several enhancement layers with the former intended for all subscribers to guarantee the basic quality of reconstructed video and the latter aiming at the quality improvement for the promising users with good channel conditions. Moreover, in order to balance user experience maximization and power consumption minimization, a novel performance metric energy utility (EU) is proposed to measure the sum achieved quality of reconstructed video at all subscribers when unit transmit power is consumed. Our objective is to maximize the system EU by jointly optimizing the intersession/interlayer subcarrier assignment and subsequent power allocation. For this purpose, we first perform subcarrier assignment for base layer and enhancement layers using greedy algorithm and then present an optimal power allocation algorithm to maximize the achievable EU using fractional programming. Simulation results show that the proposed algorithms can adaptively capture the state variations of licensed spectrum and dynamically adjust the video transmission to exploit the scarce spectrum and energy resources adequately. Meanwhile, the system EU obtained in our algorithms is greatly improved over traditional spectrum efficiency (SE) and energy efficiency (EE) optimization models.
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    • "In [10], [11], channel-aware schedulers for non-scalable video streaming have been proposed that dynamically adjust the data rates of the users and perform packet scheduling while accounting for video distortion and delay deadlines of the packets. A cross-layer framework is proposed in [12] that aims at meeting the instantaneous rate, and the deadline requirements of video traffic. In [13], [14], the problem of joint scheduling and buffer management for scalable video transmission has been addressed. "
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    ABSTRACT: This paper considers a cross-layer optimization framework for video streaming in multi-node wireless networks with time-varying interference environment. We develop a distributed joint power control and rate adaptation framework that exploits the time diversity of the wireless channels, satisfies the hard delay constraints associated to video applications, and respects a certain fairness criterion among the nodes. The proposed framework performs power allocation at the PHY/MAC layers to achieve a certain target SINR such that the difference between the arrival and the departure rates at the queues is very small, and performs video rate adaptation at the video coding layer (upper layer) according to the nodes’ demanded video quality, their channel conditions, and a given fairness criterion. A main challenge here is that the adaptation of the video rate and the power control are not performed at the same time scale. We deal with this issue and model the power and the rate variations of the nodes as linear stochastic dynamic equations, and formulate a risk-sensitive control problem that captures the hard delay constraints of the video services, and the fairness criterion for resources utilization. We provide optimal solution to this control problem, and illustrate the performance of our framework through simulations.
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    • "• Cross-layer operation aided schemes: as in [41], [61], [70], [73], [75], [76], [81], [91], [94], [103], are typically invoked for optimizing the scalable video streaming systems by considering multiple signal processing stages, such as the source-compression, FEC-encoding, modulation etc. These schemes tend to collaborate across multiple layers of the OSI stack. "
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    ABSTRACT: Layered video coding creates multiple layers of unequal importance, which enables us to progressively refine the reconstructed video quality. When the base layer (BL) is corrupted or lost during transmission, the enhancement layers (ELs) must be dropped, regardless whether they are perfectly decoded or not, which implies that the transmission power assigned to the ELs is wasted. For the sake of combating this problem, the class of inter-layer forward error correction (IL-FEC) solutions, also referred to as layer-aware FEC (LA-FEC),1has been proposed for layered video transmissions, which jointly encode the BL and the ELs, thereby protecting the BL using the ELs. This tutorial aims for inspiring further research on IL-FEC/LA-FEC techniques, with special emphasis on the family of soft-decoded bit-level IL- FEC schemes.
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