Device-to-Device (D2D) communications enable user equipments (UEs) in proximity to each other to exchange information by taking advantage of high data-rate and low energy consumption. When D2D transmissions share the radio resources with the cellular UEs, efficient admission control (AC) and radio resource allocation (RRA) strategies play a key-role in controlling the co-channel interference and allowing quality of service (QoS) provision to UEs. This paper proposes a novel joint AC and RRA strategy that provides long-term QoS support to cellular and D2D communications. The AC algorithm derives the best set of cellular and D2D links by maximizing the revenues of the service provider under QoS constraints. The RRA algorithm assigns the available channels and transmits powers to admitted users on the short-term, in order to maximize an average weighted sum-rate under the same QoS constraints of the AC. Due to the NP-hard nature of the optimization problem, we propose an AC greedy algorithm that achieves near-optimal results for reasonable numbers of D2D links. Then, we propose a low-complexity RRA algorithm that decouples channel and power allocation. Numerical results show that the proposed joint AC and RRA strategy outperforms existing frameworks by increasing up to 40% the number of satisfied cellular and D2D links and by reducing energy consumption by more than 50%.

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 user's play-out buffers.

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.

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.

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.

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.

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.