Dynamic resource allocation for scalable video multirate multicast over wireless networks

Aided by scalable video coding, multirate multicast has become a promising technique of providing differentiated quality of experience (QoE) for massive numbers of video subscribers operating in heterogeneous channel conditions. Nevertheless, due to the time-varying nature of wireless channels and the subscribers’ diverse requirements, it is challenging to dynamically control the video rate in the light of the available radio resource to achieve the best QoE. To elaborate a little further, the time scale of resource scheduling is of short-term nature, which determines the short-term video quality variation, but from a service provider’s perspective the design objective is to optimize the long-term QoE for all subscribers. Despite its importance, this problem has not been considered before. Explicitly, we formulated this problem as a time-averaged stochastic optimization problem which avoids the impact of both the short- term channel quality fluctuation and that of the video bitrates, whilst maintaining both inter- and intra- group fairness. The stratified structure of the problem inspires us to decompose it into a two-phase optimization: coarse grained assignment for each user group and fine grained assignment for each subgroup. We propose an adaptive multicast algorithm based on Lyapunov’s optimization theory for solving this problem, by striking a compelling trade-off between the system’s utility and its queue stability. We quantify the achievable performance of our proposed solution based on realistic video traces

Multi-agent deep reinforcement learning based cooperative edge caching for ultra-dense next-generation networks

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultradense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decisionprocess (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge cache

Enhancing the resilience of low Earth orbit remote sensing satellite networks

The Low earth orbit Remote Sensing (LRS) satellite network is envisioned as an essential component of bolstering space information applications, thus extending conventional ground information applications to the on-board space information applications. However, it inevitably faces grave challenges imposed by the dynamics of the satellite network’s topology, by the intermittence of Inter-Satellite communication Links (ISLs) as well as by the mobility-induced satellite-access switching of mobile terminals. Hence, this is a very different networking landscape from that of the terrestrial Internet. Against these challenging problems, we propose a resilient networking architecture for LRS Satellite Networks (LRS-SNs), with special emphasis on their dynamic routing, resilient transmission and their intrinsic mobility. Specifically, path-quality aided and lifetime-aware dynamic routing is proposed for enhancing the routing against dynamic topology changes. Hop-by-hop data transmission is relied upon for providing transmission resilience against ISL intermittence. Furthermore, data caching is invoked for providing resilience against intermittent communications imposed by dynamic satellite access switching. We employ on semi-physical simulation platform for evaluating the achievable performance of the proposed resilient network architecture

Towards reliable space-ground integrated networks: from system-level design to implementation

The integration of space and terrestrial networks results in a promising future network architecture, which exploits the high data rate and low latency of terrestrial networks as well as the wide-range coverage of satellite networks. However, spaceground integrated networks (SGINs) face some unprecedented challenges, including the rapidly fluctuating network topology, limited resources, intermittent connections, lossy links, asymmetric bandwidth allocation, and so on. The traditional end-to-end (E2E) transport protocols designed for reliable terrestrial networks exhibit inherent limitations in the context of dynamic SGIN. A promising solution is to decouple the E2E perfect reception confirmation into hop-by-hop acknowledgments, which results in prompt packet loss recovery and high transmission resilience in the face of high-dynamic topologies. Hence,in this paper we propose a reliable cache-enabled transport system, which enhances the efficiency of reliable hop-by-hop transmission, and supports multi-orbit breakpoint transmission, while at the same time facilitates the reliability of intra-satellite communication with a novel transport protocol. In addition to unveiling a compelling system-level design, we demonstrate the benefits of the proposed reliable transport system (RTS) in a real SGIN prototype relying on satellites having onboard processing capability. Our experimental results validate the feasibility of the proposed RTS in the face of lossy links, intermittent connections and intra-satellite transmission failure. Moreover, one of thesatellites has been launched in April 2021, while the other two will be launched to perform on-orbit test

Dynamic resource allocation for streaming scalable videos in SDN aided dense small-cell networks

Both wireless small-cell communications and Software-Defined Networking (SDN) in wired systems continue to evolve rapidly, aiming for improving the Quality of Experience (QoE) of users. Against this emerging landscape, weconceive scalable video streaming over SDN-aided dense smell-cell networks by jointly optimizing the video layer selection, the wireless resource allocation and the dynamic routing of video streams. In the light of this ambitious objective, we conceive a dense software-defined small-cell network architecture for the fine-grained manipulation of the video streams relying on the cooperation of small-cell base stations. Based on this framework, we formulate the scalable video streaming problem as maximizing the time-averaged QoE subject to a specific time-averaged rate constraint as well as to a resource constraint.By employing the classic Lyapunov optimization method, the problem is further decomposed into the twin sub-problems of video layer selection and wireless resource allocation. Via solving these sub-problems, we derive a video layer selection strategy and a wireless resource allocation algorithm. Furthermore, wepropose a beneficial routing policy for scalable video streams with the aid of the so-called segment routing technique in the context of SDN, which additionally exploits the collaboration of small-cell base stations. Our results demonstrate compelling performance improvements compared to the classic PID control theory based method.<br/

Sparse bandit learning based location management for space-ground integrated networks

The Space-Ground Integrated Network (SGIN) concept constitutes a promising solution for providing seamless global coverage. However, the mobility of satellites and wireless terminals imposes unprecedented challenges on the location management in SGIN. We tackle this challenge by conceiving a split identifier (ID) and Network Address (NA) based design for providing natural mobility support, and characterize the ID-NA mapping allocation problem by exploiting the storage capacity of both Geostationary Earth Orbit Satellites (GEOSs) and Low Earth Orbiting Satellites (LEOSs) to form a spatially distributed binding resolution system and optimize the caching reward in each LEOS. By considering the large quantity of ID-NA mapping and the sparsity of popular mapping having positive mean caching rewards, we formulate the mapping allocation problem as a sparse Multi-Armed Bandit (MAB) learning procedure, where the mappings are treated as the arms and the LEOSs act as the players. A distributed learning algorithm, namely the Sparse Upper confidence bound based Learning aided Caching algorithm (SULC), is proposed for estimating the mean caching rewards of mappings and selecting the optimal mappings for caching. Moreover, we derive a sub-linear upper bound of the cumulative learning regret to prove the learning efficiency of the proposed SULC. Extensive simulations have been conducted to show that the proposed SULC can quickly identify the popular mappings and provide near-optimal content hit rates. In contrast with the existing solutions, SULC has higher caching rewards and can significantly reduce the cumulative regret after a short period of learning

Proportional-fair multi-user scalable layered wireless video streaming powered by energy harvesting

The problem of adaptive multi-user scalable layered video transmission is considered in energy harvesting (EH) aided wireless communication systems. With the goal of improving the quality of video services while providing fairness amongst the users despite the random nature of both energy harvesting and the channel quality, we formulate our Scalable Video Coding (SVC) design as a Constrained Utility Function Maximization (CUFM) problem. The proportional fairness and playback smoothness of our design is guaranteed by maximizing the log-sum of the users’ video qualities, while satisfying the battery fullness constraint and video layer (quality) fluctuation constraint. By invoking the classical Lyapunov drift based optimization technique, we further decompose the CUFM problem into two parallel subproblems, i.e., a dynamic transmission power allocation problem and a dynamic layer selection problem. By solving these two subproblems, we derive a joint power allocation and video layer selection strategy for multi-user SVC video transmission. The theoretical performance bound of the proposed solution is also presented. Numerical simulations are conducted with real H.264 SVC video traces and the experimental results demonstrate the reduced playback interruption rate and layer switching rate compared to a heuristic algorithm ProNTO. The results also illustrate a tradeoff between the system’s utility function and the playback smoothness experienced by the users.<br/