HTTP streaming has become a cost effective means for multimedia delivery nowadays. In this paper, we study the use of MPEG DASH standard to stream audiovisual content. We first describe a novel estimation method for connection throughput. Then, employing the extensibility feature of DASH syntax, we present a systematic method for selecting the best audio and video alternatives given the estimated throughput. The experiments show that our solution is stable to short-term fluctuations while responding quickly to large fluctuations.

Satellite-aerial-ground integrated network (SAGIN) has been widely envisioned as a promising network architecture for 6 G. In the SAGINs, high altitude platform (HAP)-aided relaying satellite systems using hybrid free-space optics (FSO)/radio-frequency (RF) communications have recently attracted research efforts worldwide. Nevertheless, the main drawback of hybrid FSO/RF systems is the restricted bandwidth of the RF connection, especially when the FSO one is blocked by cloud coverage. This paper explores a novel solution for the hybrid FSO/RF HAP-based SAGIN under the impact of weather and atmospheric conditions. Specifically, an additional unmanned aerial vehicle (UAV) is deployed to diverse the FSO link from the HAP-to-ground station to avoid cloud blockage while maintaining a high-speed connection of the FSO link. A mirror array constructed by re-configurable intelligent surface (RIS), an emerging technology, is mounted on the UAV to reflect the signals from the HAP. The channel model of RIS-UAV takes into account both atmospheric turbulence and hovering-induced pointing errors. Furthermore, we present a novel link switching design with a multi-rate adaptation scheme for the proposed network under different weather and turbulence conditions. Numerical results quantitatively confirm the effectiveness of our proposal. Additionally, we provide insightful discussions that can be helpful for the practical system design of RIS-UAV-assisted HAP-based SAGIN using hybrid FSO/RF links. Monte Carlo simulations are also performed to validate the accuracy of theoretical derivations.

HTTP streaming has become a cost-effective means for multimedia delivery nowadays. For adaptivity to networks and terminals, a provider should generate multiple representations of an original video as well as the related metadata. Recently, there have been various adaptation methods to support adaptive HTTP streaming. In this paper, we investigate typical adaptation methods in the context of live video streaming. We first discuss the trade-off among typical adaptation methods. The evaluation and comparison are then carried out not only in terms of bitrate and buffer behaviors but also in terms of the perceptual impact on end users. It is found that the perceptual impact depends not only on adaptation method but also on the content itself. We also show that the preparation of representation sets may affect the behaviors of some adaptation methods.

This paper studies zero-forcing (ZF) precoding designs for multi-user multiple-input single-output (MU-MISO) visible light communication (VLC) broadcast channels. In such broadcast systems, the main challenging issue arises from the presence of multi-user interference (MUI) among noncoordinated users. In order to completely suppress the MUI, zero-forcing (ZF) precoding, which is originally designed for radio frequency (RF) communications, is adopted. Different from RF counterpart, VLC signal is inherently non-negative and has a limited linear range, which leads to an amplitude constraint on the input data signal. Unlike the average power constraint, obtaining the exact capacity for an amplitude-constrained channel is more cumbersome. In this paper, we first investigate lower and upper bounds on the capacity of an amplitudeconstrained Gaussian channel, which are especially tight in the high signal-to-noise (SNR) regime. Based on the derived bounds, optimal beamformer designs for the max-min fairness sum-rate and the maximum sum-rate problems are formulated as convex optimization problems, which then can be efficiently solved by using standard optimization packages.

Mobile Edge Computing (MEC) is a key technology towards delay-sensitive and computation-intensive applications in future cellular networks. In this paper, we consider a multi-user, multi-server system where the cellular base station is assisted by a UAV, both of which provide additional MEC services to the terrestrial users. Via dual connectivity (DC), each user can simultaneously offload tasks to the macro base station and the UAV-mounted MEC server for parallel computing, while also processing some tasks locally. We aim to propose an online resource management framework that minimizes the average power consumption of the whole system, considering long-term constraints on queue stability and computational delay of the queueing system. Due to the coexistence of two servers, the problem is highly complex and formulated as a multi-stage mixed integer non-linear programming (MINLP) problem. To solve the MINLP with reduced computational complexity, we first adopt Lyapunov optimization to transform the original multi-stage problem into deterministic problems that are manageable in each time slot. Afterward, the transformed problem is solved using an integrated learning-optimization approach, where model-free Deep Reinforcement Learning (DRL) is combined with model-based optimization. Via extensive simulation and theoretical analyses, we show that the proposed framework is guaranteed to converge and can produce nearly the same performance as the optimal solution obtained via an exhaustive search.

In this paper, we propose a new adaptation approach for viewport-adaptive streaming of 360-degree videos over the Internet. The proposed approach is able to systematically decide versions of tiles according to user head movements and network conditions by taking into account not only viewport estimation errors, but also users’ head movements in each segment duration. Experimental results show that the proposed approach can effectively adapt 360-degree videos to both varying network conditions and user head movements. Compared to existing approaches, the proposed approach can improve the average viewport quality by up to 3.8 dB and reduce the standard deviation of the viewport quality by up to 1.1 dB. Also, the impacts of the segment duration and the buffer size are investigated. It is found that long segment durations and large buffer sizes have significant impacts on the performances of tile selection methods.