Enhancing software-defined RAN with collaborative caching and scalable video coding

The radio access network (RAN) cache is recently proposed to improve quality of experience perceived by mobile users on Internet content access and reduce intense pressure on the backhaul network. However, because RAN caches (e.g., associated with base stations) are located below mobility management entities in mobile core network, the continuity of content service for the handover users with unfinished RANcache content transmission is destroyed. Thus motivated, we design the seamless RAN-cache handover framework based on mobility prediction algorithm (MPA), where the smart terminal with unfinished content service during mobility predicts the break and the target handover access node (such as the BS or the WiFi access point), and pre-triggers the source RAN cache to return the content service to CDN servers outside the core network (CN) or to notify the RAN cache associated with the target handover access node to prepare for serving the handover user. The framework incurs no change to cellular infrastructure including RAN and CN. Different from prior arts of assumption that users' mobility is pre-known, we design the parallel autoregressive (AR) based received signal strength (RSS) prediction approach to achieve mobility prediction, and mathematically define its false probability during different tolerant intervals. The simulation scenario deployment engine and the user movement engine are designed, considering practical issues. Through simulations, the the false probability of seamless RAN-cache handover pre-trigger through MPA is less than 1.36%, and it can be guaranteed less than 1% as the maximal RAN-cache handover processing time is increased by 8%.

The radio access network (RAN) cooperative caching, which explores the scale effect through cooperative content sharing and caching among multiple RAN caches, is considered as one effective way to fully benefit from the RAN cache. In this paper, we study the feasibility and self-organizing algorithm for RAN cooperative caching. Specifically, we first analyze the real-world dataset of daily content requests from 10 LTE Base Stations (BSs), and find that pursuing high hit rate does not guarantee the reduction of backhaul traffic. Besides, it is shown that content requests from different BSs feature strongly temporal and spatial correlations. To the best of our knowledge, this finding proves the feasibility of RAN cooperative caching for the first time. Then, based on our findings, we propose the self-organizing algorithm for RAN caches to individually decide on how to update their cached content objects, utilizing the defined utility with consideration of the link states among RAN caches, and the size and the request number of the missed and cached content objects. Finally, the performance of our proposed algorithm is validated based on real-world dataset. The results show that the proposed algorithm achieves significant improvement in reducing backhaul traffic. For instance, when cache capacity of each BS is 65% of the traffic generated by non-repeated content objects which are requested in one day over its coverage, the proposed algorithm with 8 defined cooperative caches (CoCas) reaches an average reduction of more than 60% of the backhaul traffic generated in one day over the BS's coverage.

The rapid growth of Internet contents from wireless data networks, especially social media, results in unprecedented traffic volume, invoking a challenge to the load of cellular infra-structure. Moreover, instead of traditional quality of service (QoS), the demands of quality of experience (QoE) become a more practical norm, which could barely be improved under present resource allocation on radio spectrum. With this in mind, we more generally consider resource allocation by exploiting caching in radio access networks (RANs), and show that caching, as a sort of storage, could be viewed as a substitution of the communication spectrum. Thus, we propose a collaborative strategy to implement caching in infrastructure and in mobile devices simultaneously, which in general turns out to be device-to-device (D2D) communication. This new design paradigm enables great reduction of latency for requesting Internet contents and can be implemented via slight amendments to present cellular systems.

International audience

HTTP adaptive streaming (HAS) is a state-of-the-art technology for video streaming. Optimal adaptive streaming schemes should be designed to maximize the quality of experience (QoE) in network environments with unstable bandwidths such as wireless networks. QoE is degraded for Advanced Video Coding (AVC)-based HAS due to frequent quality switching and rebuffering in wireless networks. Meanwhile, Scalable Video Coding (SVC)-based HAS can maximize the QoE by flexibly requesting the base layer (BL) and enhancement layer (EL) based on variable network conditions. However, in SVC-based HAS, the required bandwidth is significantly high due to the encoding overhead. In this paper, we propose a layer-assisted video quality adaptation for improving QoE in wireless networks. The proposed scheme employs a video encoding method, composed of multiple BLs and ELs, using both AVC and SVC. The quality of the BL is determined using the buffer occupancy level and measured bandwidth to minimize rebuffering. The quality of the EL is determined using the buffer region and segment quality differences to minimize instability. enhancement dummy (ED) consists of the selected ELs. The layer scheduler decides the segment that is to be requested based on the QoE model according to the BL and ED to maximize the QoE. Experimental results show that the proposed scheme achieves high QoE compared to the existing schemes because the instability is low and no rebuffering occurs.

This paper describes the design and implementation of a multimedia home gateway for threescreen television (3STV) service. The proposed in-home wireless network uses scalable video coding (SVC) and unequal error protection with Raptor forward error correction (FEC) for maximizing the quality of experience (QoE) over the variable-bandwidth, error-prone wireless network. The gateway incorporates (a) dynamic SVC layerswitching, which enables the server to perform selecting appropriate layers from SVC bitstreams, (b) adaptive Raptor FEC, which controls the overhead of Raptor FEC according to packet loss rate (PLR), (c) an efficient combination of (a) and (b), and (d) slice group-based selective streaming in the overall gateway architecture. The paper explains the home gateway architecture as well as experiments for performance evaluation as compared to that of traditional SVC streaming. In the experiments conducted, gains in video quality vary from 2 to 5dB in peak signal-to-noise ratio (PSNR), with corresponding subjective improvements. Overall reductions of bit rate at the input to the home gateway vary from 28% to 36% <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Energy-efficient and quality of experience (QoE) are extremely important for video services. However, most studies just consider one of them. This paper proposes an adaptive energy-efficient and user's quality of experience optimization method to maximize the overall satisfaction of video services. Video content is encoded by the scalable video coding (SVC), which can provide mobile video services at different quality levels with associated energy consumption. Based on SVC, the power consumption model and video quality model of mobile video services are established. Then a utility function is proposed to measure overall satisfaction, which is defined to weight the energy consumption and the video quality. QoE and energy consumption are jointly optimized by dynamically triggering the layer switching during the video reproduction. The particle swarm optimization (PSO) algorithm is used to obtain the optimal solution. Simulation results show proposed method can achieve significant QoE levels and higher energy efficiency for mobile users.

Scalable video coding (SVC) has aroused a wide interest in the areas of video coding and transmission technology, since it provides desirable features for heterogeneous error-prone network environments. The layered video structure allows not only adaptation to the available bandwidth but also a device adaptation capability via multiple decodable sub-streams. In this paper, we focus on SVC Medium- Grain Scalability (MGS) and investigate the end user's quality of experience (QoE) in error-prone transmission conditions. We describe our error detection and concealment implementation on the JSVM 9.15 reference decoder and assess its effectiveness through several quality indicators. Results show an improvement of the proposed concealment strategy when compared to the standard Frame Copy (FC) and Interpolation, in terms of Peak Signal-to-Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) quality metrics. This improvement is evident in the case of concealment on quality enhancement layers, since video fluidity is preserved assuring an acceptable QoE to the end users.

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.

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users' Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users' QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.

The transmission of video data is a major part of traffic on today's Internet. Since the Internet is a highly dynamic environment, quality adaptation is essential in matching user device resources with the streamed video quality. This can be achieved by applying mechanisms that follow the Scalable Video Coding (SVC) standard, which enables scalability of the video quality in multiple dimensions. In SVC-based streaming, adaptation decisions have long been driven by Quality of Service (QoS) metrics, such as throughput. However, these metrics do not well match the way human users perceive video quality. Therefore, in this paper, the classical SVC-based video streaming approach is expanded to consider Quality of Experience (QoE) for adaptation decisions. The video quality is assessed using existing objective techniques with a high correlation to the human perception. The approach is evaluated in context of a P2P-based Video-on-Demand (VoD) system and shows that by making peers favor always layers with a high estimated QoE but not necessarily high bandwidth requirements, the performance of the entire system can be enhanced in terms of playback delay and SVC video quality by up to 20%. At the same time, content providers are able to reduce up to 60 of their server costs, compared to the classical QoS-based approach.

To reduce network access latency, network traffic volume, and server load, caching capacity was proposed as a component of eNodeBs in the radio access network (RAN). These eNodeB caches require less transport energy but bring additional caching energy through providing each eNodeB with caching capacity. It is thus challenging for eNodeBs to make content placement and request routing decisions so as to minimize the total energy consumption, especially when considering different caching hardware technologies, arrival rate of requests and content popularity. To address this problem, we first build an energy model to formulate the problem of minimizing energy consumption at eNodeB caches. Then a Lagrangian relaxation technique is adopted to find a near-optimal solution to the proposed model. Based on the proposed model and the obtained solution, we design a practical scheme for coordinating the content placement and request routing, and thus ensure the minimum-energy eNodeB caches. Compared with the existing works, our proposal significantly reduces the energy consumption by approximately 28% while keeps a good network performance. Our results also indicate that caching hardware technologies and content popularity greatly affect the content placement, therefore are crucial to the energy efficiency of eNodeB caches.

Multicasting multiple video streams over wireless broadband access networks enables the delivery of multimedia content to large-scale user communities in a cost-efficient manner. Three dimensional (3D) videos are the next natural step in the evolution of digital media technologies. In order to provide 3D perception, 3D video streams contain one or more views that greatly increase their bandwidth requirements. Due to the limited channel capacity and variable bit rate of the videos, multicasting multiple 3D videos over wireless broadband networks is a challenging problem. In this article, we consider a 4G wireless access network in which a number of 3D videos represented in two-view plus depth format and encoded using scalable video coders are multicast. We formulate the optimal 3D video multicasting problem to maximize the quality of rendered virtual views on the receivers' displays. We show that this problem is NP-complete and present a polynomial time approximation algorithm to solve it. We then extend the proposed algorithm to efficiently schedule the transmission of the chosen substreams from each video in order to maximize the power saving on the mobile receivers. Our simulation-based experimental results show that our algorithm provides solutions that are within 0.3 dB of the optimal solutions while satisfying real-time requirements of multicast systems. In addition, our algorithm results in an average power consumption reduction of 86%.

Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks.

The heterogeneous vehicular network (HetVNET), which consists of multiple radio access networks (RANs), is a promising paradigm to provide a variety of services on the road. However, with the diverse quality of experience (QoE) requirements for vehicles, how to choose the optimal network for the vehicles pose great challenges. In this paper, we investigate the network selection problem in a HetVNET, which includes LTE-vehicle-to-anything (LTE-V2X), dedicated short-range communications (DSRC), WiFi. The network selection problem is formulated as a stable matching between the vehicles and different RANs. A two-sided one-to-many matching algorithm is presented based on the preference lists of both vehicles and RANs. Numerical simulation results show that the proposed method can improve the total throughput of the system and reduce the total network switch time compared to some existing algorithms.