The weighted Euclidean norm $||x||_w$ of a vector $x\in \mathbb{R}^d$ with weights $w\in \mathbb{R}^d$ is the Euclidean norm where the contribution of each dimension is scaled by a given weight. Approaches to dimensionality reduction that satisfy the Johnson–Lindenstrauss (JL) lemma can be easily adapted to the weighted Euclidean distance if weights are known and fixed: it suffices to scale each dimension of the input vectors according to the weights, and then apply any standard approach. However, this is not the case when weights are unknown during the dimensionality reduction or might dynamically change. In this paper, we address this issue by providing a linear function that maps vectors into a smaller complex vector space and allows to retrieve a JL-like estimate for the weighted Euclidean distance once weights are revealed. Our results are based on the decomposition of the complex dimensionality reduction into several Rademacher chaos random variables, which are studied using novel concentration inequalities for sums of independent Rademacher chaoses.

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.

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.

We describe an optimization technique for multiuser resource allocation assuming adaptive modulation and coding (AMC) in OFDMA radio downlink communications. The resource allocation process is based on the dual solution of a weighted sum-rate maximization problem. In particular, to realistically address IP-based video and data communications, the set of considered constraints include the number of bits actually present in the different transmission buffers. Furthermore, we propose an innovative AMC solution, based on M-QAM modulation and LDPC codes, jointly working with the scheduler and capable to maximize the throughput while guaranteeing a given set of performance requirements. A simple weight adaptation algorithm is introduced to properly tune the trade off between opportunistic transmissions and fairness among users. Several simulation results are presented to validate the proposed approach and a comparison with more traditional schemes is provided. In particular, we show that a good trade off between user fairness and opportunistic exploitation of the radio channel can be reached with the proposed strategies, improving the communication quality for both real-time video and non real-time data applications.

In this work we describe an optimization technique for multiuser resource allocation and adaptive modulation and coding (AMC) in OFDMA radio downlink communications. In particular, we propose an innovative AMC solution, based on M-QAM modulation and LDPC codes, jointly working with the scheduler and capable to maximize the achieved throughput while guaranteeing a given set of BER/FER requirements. Simulation results are presented to validate the proposed approach and a comparison with more traditional schemes is provided.