A Framework for Quality-Driven Delivery in Distributed Multimedia Systems

In this paper, we propose a framework for Quality-Driven Delivery (QDD) in distributed multimedia environments. Quality-driven delivery refers to the capacity of a system to deliver documents, or more generally objects, while considering the users expectations in terms of non-functional requirements. For this QDD framework, we propose a model-driven approach where we focus on QoS information modeling and transformation. QoS information models and meta-models are used during different QoS activities for mapping requirements to system constraints, for exchanging QoS information, for checking compatibility between QoS information and more generally for making QoS decisions. We also investigate which model transformation operators have to be implemented in order to support some QoS activities such as QoS mapping

A Generalized Convolution with a Weight Function for the Fourier Cosine and Sine Transforms

A generalized convolution with a weight function for the Fourier cosine and sine transforms is introduced. Its properties and applications to solving a system of integral equations are considered

Enabling architectures for QoS provisioning

Nowadays, new multimedia services have been deployed with stringent requirements for Quality of Service (QoS). The QoS provisioning is faced with the heterogeneity of system components. This thesis presents two research: on architectures for QoS management at the application layer, fulfilled mainly by software components; and on distributed software architectures for routing devices providing desired QoS at the underlying communication layer. At the application layer, the QoS architecture we propose, based on the Quality Driven Delivery (QDD) framework, deals with the increasing amount of QoS information of a distributed system. Based on various QoS information models we define for key actors of a distributed system, a QoS information base is generated using QoS information collecting and analysis tools. To translate QoS information among different components, we propose mechanisms to build QoS mapping rules from statistical data. Experiments demonstrate that efficient QoS decisions can be made effectively regarding the contribution of all system components with the help of the QoS information management system. At the underlying layer, we investigate distributed and scalable software architectures for QoS-enabled devices. Due to the huge volume of traffic to be switched, the traditional software model used for current generation routers, where the control card of the router performs all the processing tasks, is no longer appropriate in the near future. We propose a new scalable and distributed architecture to fully exploit the hardware platforms of the next generation routers, and to improve the quality of routers, particularly with respect to scalability and to a lesser extent to resiliency and availability. Our proposal is a distributed software framework where control tasks are shared among the control and line cards of the router. Specific architectures for routing, signaling protocols and routing table management are developed. We investigate the challenges for such distributed architectures and proposed various solutions to overcome them. Based on a general distributed software framework, an efficient scalable distributed architecture for MPLS/LDP and different scalable distributed schemes for the routing table manager (RTM) are developed. We also evaluate the performance of proposed distributed schemes and discuss where to deploy these architectures depending on the type of routers (i.e., their hardware capacity

Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN

Today, vehicles use smart sensors to collect data from the road environment. This data is often processed onboard of the vehicles, using expensive hardware. Such onboard processing increases the vehicle's cost, quickly drains its battery, and exhausts its computing resources. Therefore, offloading tasks onto the cloud is required. Still, data offloading is challenging due to low latency requirements for safe and reliable vehicle driving decisions. Moreover, age of processing was not considered in prior research dealing with low-latency offloading for autonomous vehicles. This paper proposes an age of processing-based offloading approach for autonomous vehicles using unsupervised machine learning, Multi-Radio Access Technologies (multi-RATs), and Edge Computing in Open Radio Access Network (O-RAN). We design a collaboration space of edge clouds to process data in proximity to autonomous vehicles. To reduce the variation in offloading delay, we propose a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to edge clouds when its local resources are insufficient. We formulate an optimization problem for age-based offloading that minimizes elapsed time from generating tasks and receiving computation output. To handle this non-convex problem, we develop a surrogate problem. Then, we use the Lagrangian method to transform the surrogate problem to unconstrained optimization problem and apply the dual decomposition method. The simulation results show that our approach significantly minimizes the age of processing in data offloading with 90.34 % improvement over similar method

PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

In a frequency division duplexing multiple-input multiple-output (FDD-MIMO) system, the user equipment (UE) send the downlink channel state information (CSI) to the base station for performance improvement. However, with the growing complexity of MIMO systems, this feedback becomes expensive and has a negative impact on the bandwidth. Although this problem has been largely studied in the literature, the noisy nature of the feedback channel is less considered. In this paper, we introduce PRVNet, a neural architecture based on variational autoencoders (VAE). VAE gained large attention in many fields (e.g., image processing, language models, or recommendation system). However, it received less attention in the communication domain generally and in CSI feedback problem specifically. We also introduce a different regularization parameter for the learning objective, which proved to be crucial for achieving competitive performance. In addition, we provide an efficient way to tune this parameter using KL-annealing. Empirically, we show that the proposed model significantly outperforms state-of-the-art, including two neural network approaches. The proposed model is also proved to be more robust against different levels of noise

Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks \cite{10045045}, which requires routing. Since edge clouds do not have the same available computation resources and tasks' computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD

A QoS mapping rule builder

Although many QoS management architectures have been recently introduced with a lot of advanced features, they have never been widely used in the existing applications due to the lack of interoperation between providers and users, or between network operators. One of the main issues is the heterogeneity of QoS information coming from different sources: clients, communication networks, servers, data .etc. In the context of Quality-Driven Delivery (QDD) referring to the ability of a system to deliver data objects while considering the end-users expectations, all components of a distributed multimedia system have to contribute to satisfy users requirements. The mapping activity is therefore essential for dealing with the variety of QoS information of these components. In this paper, we propose an approach aimed at creating QoS mapping rules using statistical data analysis and data mining techniques combined with monitoring tools. The automatic generation of QoS mapping rules allows adapting the QoS management architectures to different environments as well as different classes of users