Sergio Cicalo

Distortion-Fair Cross-Layer Resource Allocation for Scalable Video Transmission in OFDMA Wireless Networks

The design of optimized video delivery to multiple users over a wireless channel is a challenging task, especially when the objectives of maximizing the spectral efficiency and providing a fair video quality have to be jointly considered. In this paper we propose a novel cross-layer optimization framework for scalable video delivery over OFDMA wireless networks. It jointly addresses rate adaptation and resource allocation with the aim of maximizing the sum of the achievable rates while minimizing the distortion difference among multiple videos. After having discussed the feasibility of the optimization problem, we consider a “vertical” decomposition of it and propose the iterative local approximation (ILA) algorithm to derive the optimal solution. The ILA algorithm requires a limited information exchange between the application and the MAC layers, which independently run algorithms that handle parameters and constraints characteristic of a single layer. In order to reduce the overall complexity and the latency of the optimal algorithm, we also propose suboptimal strategies based on the first-step of the ILA algorithm and on the use of stochastic approximations at the MAC layer. Our numerical evaluations show the fast convergence of the ILA algorithm and the resulting small gap in terms of efficiency and video quality fairness between optimal and suboptimal strategies. Moreover, significant individual PSNR gains, up to 7 dB for high-complexity videos in the investigated scenario, are obtained with respect to other state-of-the-art frameworks with similar complexity.

Centralized vs Distributed Resource Allocation in Multi-Cell OFDMA Systems

Radio Resource Management with Inter-cell Interference Coordination (ICIC), is a key issue under investigation for next generation wireless systems such as Long Term Evolution (LTE). Although centralized resource allocation (RA) and collaborative processing can optimally perform ICIC, the overall required complexity suggests the consideration of distributed techniques. In this paper we propose and compare a centralized RA strategy aimed at maximizing the sum-rate of a multi-cell clustered system in presence of power and fairness constraints, and a distributed RA strategy where inter-cell interference is partially coordinated through power planning schemes with preassigned power (an example is fractional frequency reuse). The distributed RA strategy reduces both signaling and feedback requirements, preserves intra-cell fairness and jointly works with a load balancing algorithm to support inter-cell fairness. We show in the results that the distributed RA with aggressive frequency reuse is able to approach the performance of the centralized RA when the number of users is large, while preserving the same level of fairness.

Quality-fair HTTP adaptive streaming over LTE network

In HTTP adaptive streaming (HAS) applications multiple video clients sharing the same wireless channel may experience different video qualities as result of both different video content complexity and different channel conditions. This causes unfairness in the end-user video quality. In this paper, we propose a quality-fair adaptive streaming (QFAS) solution to deliver fair video quality to HAS clients competing for the same resources in an LTE cell. In the QFAS framework the share of radio resource is optimized according to video content characteristics and channel condition. The proposed solution is compared with other state-of-the-art strategies and numerical results in terms of SSIM quality metric shows that it significantly improves the quality fairness among heterogeneous HAS users.

Improving QoE and Fairness in HTTP Adaptive Streaming Over LTE Network

HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the users play-out buffers.

Fairness-oriented multi-stream rate adaptation using scalable video coding

In the delivery of video services like video on-demand, IP-TV, sport broadcasting, as well as real-time streaming, the end-user expectation is to receive the best feasible quality independently of the particular video complexity, even in the presence of packet losses. In this scenario, rate adaptation is required to optimize the overall quality, whereas fairness is an important issue that has to be addressed. In this paper we propose a multi-stream rate adaptation framework with reference to the scalable video coding (SVC) extension of the H.264/AVC standard with medium grain scalability (MGS). We first define a general discrete multi-objective problem with the aim to maximize the sum of assigned rates, while minimizing the differences among the expected distortions, under a total bit-rate constraint. A single-objective problem formulation is then derived by applying a continuous relaxation. Finally, a simplified continuous semi-analytical model that accurately estimates the rate-distortion relationship for both error-free channel and packet-erasure channel is also proposed, which allows us to derive an optimal and low-complexity procedure to solve the relaxed problem. Unequal erasure protection (UXP) is also considered and designed to suitably shape the rate-distortion relationship for different values of RTP packet-loss rate. The numerical results show the goodness of our framework in terms of error gap between the relaxed and its related discrete solution, and the significant performance improvement achieved with respect to an equal-rate adaptation scheme.

Multiple Video Delivery in m-Health Emergency Applications

M-health services are expected to become increasingly relevant in the management of emergency situations by enabling real-time support of remote medical experts. In this context, the transmission of multiple health-related video streams from an ambulance to a remote hospital can improve the efficacy of the teleconsultation service, but requires a large bandwidth to meet the desired quality, not always guaranteed by the mobile network. In order to deliver the multiple streams over a single bandwidth-limited wireless access channel, in this paper we propose a novel optimization framework that enables to classify the available video sources and to automatically select and adapt the best streams to transmit. The camera ranking algorithm jointly works with a cross-layer adaptation strategy for multiple scalable streams to achieve different objectives and/or tradeoffs in terms of number and target quality of the transmitted videos. The final goal of the optimization is to dynamically adjust the overall transmitted throughput to meet the actual available bandwidth, while being able to provide high quality to diagnostic video sequences and lower quality to less critical ambient videos. Numerical simulations considering a realistic emergency scenario with long term evolution advanced (LTE-A) connectivity show that the proposed content/context-aware solution is able to automatically select the best sources of information from a visual point of view and to achieve optimal end-to-end video quality for both the diagnostic and the ambient videos.

Content/Context-aware Multiple Camera Selection and Video Adaptation for the Support of m-Health Services☆

Abstract In this paper we focus on the problem of delivering multiple health-related real-time video streams from an emer- gency scenario to a remote hospital by exploiting the uplink of an LTE wireless access network, in order to support efficient m-health tele-consultation services. In this context, the transmission of health-related information is a chal- lenging task, due to the variability and the limitations of the mobile radio link, the different qualities of the visual representations of the cameras and the heterogeneous end-to-end quality requirements of the contents to be delivered. We propose a solution based on: (i) a context-aware camera selection algorithm, which selects among the cameras deployed in the emergency scenario one or more video sources taking into account specific ranking criteria mainly related to the quality of the visual representation of the object of interest; (ii) a content-aware technique for the trans- mission of multiple scalable videos that jointly considers video aggregation and adaptation at the application layer of the transmitting equipment and takes into account the different quality requirements of diagnostic and ambient videos. Numerical results show that the proposed strategy permits to achieve a good end-to-end quality for both the diagnostic and the ambient videos even in the presence of rate limitations and fluctuations in the wireless link, due to the channel variations and the traffic load inside the LTE cell. When the wireless link capacity decreases, the proposed strategy appropriately discards the videos coming from the cameras providing the lowest visual quality, according to the camera ranking results, and, at the same time, adapts the rate of the transmitted videos to provide the requested quality with priority to diagnostic content.

Adaptive Resource Allocation With Proportional Rate Constraints for Uplink SC-FDMA Systems

In this letter, we address the problem of ergodic sum-rate maximization under proportional rate constraints for the uplink of single-carrier frequency-division multiple-access (SC-FDMA) systems. Finding optimal solution generally requires high computational complexity, because SC-FDMA imposes the contiguous allocation of the available frequency resources. To reduce complexity, we propose a novel suboptimal algorithmic solution, based on Lagrangian relaxation of the rate constraints, which exploits a simple but effective estimation of the average number of the resources to allocate in order to reduce the search space. The complexity of the resulting algorithm increases only linearly with the number of users and the number of resources, whereas the performance gap to optimal solution is limited to the 10% of the sum-rate.

Cross-layer optimization for m-health SVC multiple video transmission over LTE uplink

M-health services are expected to become increasingly relevant in the management of emergency situations, enabling real-time support of remote medical experts. In this context, the transmission of health-related information from an ambulance to a remote hospital is a challenging task, due to the variability and the limitations of the mobile radio link. In particular, the transmission of multiple video streams can improve the efficacy of the tele-consultation service, but requires a large bandwidth to meet the desired quality, not always guaranteed by the mobile network. In this paper we propose a novel cross-layer adaptation strategy for multiple SVC videos delivered over a single LTE channel, which dynamically adjusts the overall transmitted throughput to meet the actual available bandwidth, while being able to provide high quality to diagnostic video sequences and lower (but fair) quality to less critical ambient videos. After having introduced a realistic LTE uplink scenario, including an advanced resource allocation strategy, we show through numerical simulations that the proposed solution is capable to achieve an optimal end-to-end video quality for both the diagnostic and the ambient videos.

Fair resource allocation with QoS support for the uplink of LTE systems

Resource allocators that maximize ergodic sum-rate under proportional rate constraints have been recently introduced and analysed for both OFDMA and SC-FDMA wireless systems. They are able to provide long-term fairness by assigning to users, on average, a predefined share of the available system capacity. However, the short-term fairness of these schedulers with its effect on the delays, and their efficiency in presence of bursty traffic may be open issues for a possible application in 4G wireless networks. In this paper we address these issues by proposing some solutions that make such resource allocators able to support both GBR and best effort traffic. We consider the uplink of a LTE single cell scenario with some realistic conditions (e.g., discrete rate assignment, power control) and evaluate the performance of the proposed solutions in presence of heterogeneous traffic by using QoS-aware proportional fairness based schedulers as benchmark. The results show that the proposed resource allocator is able to achieve an high number of satisfied GBR users with a significant reduction of the packet delay without starving non-GBR users.