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


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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    • "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.
    IEEE Transactions on Vehicular Technology 09/2015; DOI:10.1109/TVT.2015.2480785 · 1.98 Impact Factor
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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.
    IEEE Communications Surveys &amp Tutorials 04/2015; 17(2):1-1. DOI:10.1109/COMST.2015.2392378 · 6.81 Impact Factor
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    • "Multiuser video transmission in a wireless environment has been extensively investigated in the literature [5, 6, 11– 31]. The publications in this area, when considering the video encoding, can be categorized into those that consider transmission of scalable video [11] [12] [13] [14], transmission of preencoded single layer video [5, 6, 15–21], smoothing based [32] [33], video trace based system level simulations [22] [23] [24], testbed oriented approach [31] (preencoded video is transmitted but the implementation issues are the main interest), and those that consider change of the parameter of the single layer video encoders immediately prior to transmission [26– 30]. Only the work from the last category can be strongly correlated to our work, but the publications in this category use rate distortion models applicable only for long term rate adaptation, unsuitable for real time frame by frame adaptation due to their low precision and large complexity or do not explain how the adaptation is carried out. "
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    ABSTRACT: We present a framework for cross-layer optimized real time multiuser encoding of video using a single layer H.264/AVC and transmission over MIMO wireless channels. In the proposed cross-layer adaptation, the channel of every user is characterized by the probability density function of its channel mutual information and the performance of the H.264/AVC encoder is modeled by a rate distortion model that takes into account the channel errors. These models are used during the resource allocation of the available slots in a TDMA MIMO communication system with capacity achieving channel codes. This framework allows for adaptation to the statistics of the wireless channel and to the available resources in the system and utilization of the multiuser diversity of the transmitted video sequences. We show the effectiveness of the proposed framework for video transmission over Rayleigh MIMO block fading channels, when channel distribution information is available at the transmitter.
    Advances in Multimedia 01/2014; DOI:10.1155/2014/362196
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