A cross-layer approach to enhance QoS for multimedia applications over satellite

The need for on-demand QoS support for communications over satellite is of primary importance for distributed multimedia applications. This is particularly true for the return link which is often a bottleneck due to the large set of end-users accessing a very limited uplink resource. Facing this need, Demand Assignment Multiple Access (DAMA) is a classical technique that allows satellite operators to offer various types of services, while managing the resources of the satellite system efficiently. Tackling the quality degradation and delay accumulation issues that can result from the use of these techniques, this paper proposes an instantiation of the Application Layer Framing (ALF) approach, using a cross-layer interpreter(xQoS-Interpreter). The information provided by this interpreter is used to manage the resource provided to a terminal by the satellite system in order to improve the quality of multimedia presentations from the end users point of view. Several experiments are carried out for different loads on the return link. Their impact on QoS is measured through different application as well as network level metrics

Efficient memory management in video on demand servers

In this article we present, analyse and evaluate a new memory management technique for video-on-demand servers. Our proposal, Memory Reservation Per Storage Device (MRPSD), relies on the allocation of a fixed, small number of memory buffers per storage device. Selecting adequate scheduling algorithms, information storage strategies and admission control mechanisms, we demonstrate that MRPSD is suited for the deterministic service of variable bit rate streams to intolerant clients. MRPSD allows large memory savings compared to traditional memory management techniques, based on the allocation of a certain amount of memory per client served, without a significant performance penaltyPublicad

The design and implementation of a multimedia storage server tosupport video-on-demand applications

In this paper we present the design and implementation of a client/server based multimedia architecture for supporting video-on-demand applications. We describe in detail the software architecture of the implementation along with the adopted buffering mechanism. The proposed multithreaded architecture obtains, on one hand, a high degree of parallelism at the server side, allowing both the disk controller and the network card controller work in parallel. On the other hand; at the client side, it achieves the synchronized playback of the video stream at its precise rate, decoupling this process from the reception of data through the network. Additionally, we have derived, under an engineering perspective, some services that a real-time operating system should offer to satisfy the requirements found in video-on-demand applications.This research has been supported by the Regional Research Plan of the Autonomus Community of Madrid under an F.P.I. research grant.Publicad

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations