This paper investigates the influence of the video quality assessment at the medium access control (MAC) layer of an 802.11e distributed network in which the purpose is to adapt the retry limit of each video packet for maximizing the quality perceived by the end user. Moving from an existing Markov model of the 802.11e access scheme, the number of retransmissions associated to each video packet is evaluated to satisfy the distortion requirements inferred using two quality assessment measures: the mean squared error (MSE) and the structural similarity (SSIM) index. The aim of the proposed work is to discuss the suitability of these measures for managing the retransmission policy in the presence of contention-based mechanisms. The numerical results are obtained using a network simulator implementing the 802.11e enhanced distributed channel access (EDCA) in which the video sequences are encoded using the H.264 scalable video coding (SVC) standard. © 2014 IEEE

No abstract

To build intelligent model learning in conventional architecture, the local data are required to be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage of personalization, and insufficient use of network resources. To address these issues, federated learning (FL) is introduced by offering a systematical framework that converges the distributed modeling process between local participants and the parameter server. However, the challenging issues of insufficient participant scheduling, aggregation policies, model offloading, and resource management still remain within conventional FL architecture. In this survey article, the state-of-the-art solutions for optimizing the orchestration in FL communications are presented, primarily querying the deep reinforcement learning (DRL)-based autonomy approaches. The correlations between the DRL and FL mechanisms are described within the optimized system architectures of selected literature approaches. The observable states, configurable actions, and target rewards are inquired into to illustrate the applicability of DRL-assisted control toward self-organizing FL systems. Various deployment strategies for Internet of Things applications are discussed. Furthermore, this article offers a review of the challenges and future research perspectives for advancing practical performances. Advanced solutions in these aspects will drive the applicability of converged DRL and FL for future autonomous communication-efficient and privacy-aware learning.