Hybrid Multicast and Segment-Based Caching for VoD Services in LTE Networks

Wireless video broadcast/multicast and mobile video communication pose a challenge to the conventional video transmission scheme (which consists of separate digital source coding and digital channel coding). The reason is that the separate coding scheme is based on nonscalable coding design, and this unavoidably leads to cliff effect as well as limits the ability to support multiple users with diverse channel conditions. In this paper, we propose a novel wireless scalable video coding (WSVC) framework. Specifically, we present a hybrid digital-analog (HDA) joint source-channel coding (JSCC) scheme that integrates the advantages of digital coding and analog coding. Moreover, the proposed JSCC is able to broadcast one video with different resolutions to fit various devices with different display resolutions. Compared to most state-of-the-art video delivery methods, it avoids the staircase effect and realizes continuous quality scalability (CQS) on condition that the channel quality is within the expected range, and it has strong adaptability to channel variation with higher coding efficiency and better fairness among all receivers. Therefore, it is very suitable for wireless video broadcast/multicast transmission and mobile video applications. The experimental results show that for broadcasting/multicasting the videos with CIF and QCIF resolutions the proposed WSVC outperforms SoftCast (which is a new analog scheme) average 0.60-5.90 dB and 3.39-9.97 dB respectively, outperforms SVC+HM (which combines H.264/SVC codec and hierarchical modulation technique) average 3.87-9.13 dB and 0.27-10.47 dB respectively, and outperforms DCast (which is an up-to-date video delivery scheme) about 0.2-3.3 dB for the video with CIF resolution. The experimental results verify the effectiveness of our proposed WSVC framework.

Long Term Evolution (LTE) packet scheduling plays an essential role as part of LTE's Radio Resource Management (RRM) to support successful implementation of new data services across the LTE network. The main contribution of this paper is to model and propose a novel scheduling scheme for LTE networks and to compare its performance with the performances of both the Best-CQI and RR Uplink schedulers. The Best-CQI scheduler is characterized by high data rates at cell level, but poor fairness. On the other hand, the round robin (RR) scheduler is characterized by low data rates at cell level, but good fairness. The main goal of our proposed scheme is to process these two conflicts terms in a better way. Performance modeling results presented in the paper show that the newly proposed scheduling scheme allows fair distribution of available LTE resources while at the same time keeps the system's capacity utilization as good as possible.

In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

Caching and optimized request routing in cloud-based content delivery systems

One of the challenges of the mobile industry is to cope with the growth of mobile traffic demand expected for the next years, primarily driven by the increasing usage of mobile video services. Indeed, the existence of increasingly powerful terminals is encouraging the consumption of high-quality video content. Usually, video services are identified with linear Television (TV) and scheduled broadcast (point-to-multipoint (p-t-m)) distribution. However, the consumption of video content over mobile networks is different from traditional fixed TV because contents are mainly consumed on-demand with unicast point-to-point (p-t-p) connections. Then, the convergence of linear TV and on-demand content delivery represents a challenge that requires a combined broadcast/unicast transmission model. This dissertation addresses the use of broadcasting technologies for the provision of mobile multimedia services in Fourth Generation (4G) mobile broadband networks and beyond. Specifically, the dissertation focuses on the broadcast technology included in 4G Long Term Evolution (LTE) and LTE Advanced (LTE-A) networks, known as Enhanced Multicast Broadcast Multimedia Services (eMBMS). It analyses the benefits of the eMBMS physical layer aspects regarding Multimedia Broadcast Multicast Services over a Single Frequency Network (MBSFN) deployments and identifies the current limitations of eMBMS at physical layer by comparing with the broadcast technology of the other 4G mobile system, the Institute of Electrical and Electronics Engineers (IEEE) 802.16m standard. Those limitations are the use of a dedicated carrier and Multiple-Input Multiple-Output (MIMO) techniques for broadcast transmissions. Our investigations employ a complete simulation platform including link-level and system-level simulations to evaluate the performance of broadcast transmissions in these real technologies. The research on eMBMS services is aimed at finding the optimum delivery of streaming and file download services focusing on the Radio Resource Management (RRM) problem and trade-off between Physical layer ? Forward Error Correction (PHY-FEC) and Application Layer - Forward Error Correction (AL-FEC). Concerning streaming services, results show that the use of AL-FEC increases the coverage level and, then, the maximum service data rate. The gain due to AL-FEC is greater in scenarios with high mobility users, although, this gain is limited if low zapping times are desired. Regarding file delivery services, this dissertation analyses the duration of the transmission required to guarantee the correct file reception and the reduction in the mean throughput of unicast users with different delivery modes. They are the unicast delivery, the eMBMS delivery and a hybrid approach, which combines a first eMBMS delivery with a post-delivery error repair phase with unicast transmissions. Our results show that the hybrid delivery is the most efficient configuration in terms of file download time, although it further reduces unicast performance. On the other hand, as an exemplary use case, this dissertation also investigates the use of LTE networks for the provision of vehicular safety services comparing both unicast and eMBMS delivery modes. Results highlight the significant benefits in terms of resource usage, end-to-end latency and cost delivery saving that can be achieved by using eMBMS for the delivery of road safety applications. In addition, research also addresses the problem associated with the support of road safety applications over the current eMBMS architecture, the configuration of the Intelligent Transportation Systems (ITS) server in charge of distributing safety messages as well as on its interaction with the mobile network operator. Finally, this dissertation analyzes feasible options of convergence between mobile and broadcast industry to ensure the success of the mobile broadcasting deployments in the future. A separated evolution of the broadcast technologies of both industries would lead to a scenario with two complete different industries, with different network infrastructures and business models competing for market and spectrum. This dissertation proposes an approach by which the definition of the future Fifth Generation (5G) mobile broadband communication system could bring together the cellular and broadcast industries to form a single fixed and mobile converged network and offer a full alternative to terrestrial TV broadcasting as a universal service

In this paper, we address the issue of how to leverage Wi-Fi Direct (as an outband solution) to enable the Device-to-Device (D2D) communication, which can not only offload massive data traffic from the LTE (Long Term Evolution)-based cellular network, but also support the communications of Internet-of-Things (IoT) applications. Particularly, we develop a clustering-based scheme that automatically finds the best candidates to remain connected to the LTE network while the rest of the devices can be disconnected directly from the LTE-based cellular network. By doing so, we can reduce the signal interference, increase the average throughput and spectral efficiency of the network, and also reduce unnecessary data traffic that can be transmitted locally by D2D communications instead of going through the LTE-based cellular network. Devices in established clusters can indirectly communicate with the LTE network via the cluster head, which can be dynamically selected and remains connected to the LTE network directly. Using the real-world cellular data collected from a public database related to the deployment of LTE networks, and Google geo-coding APIs (Application Program Interfaces) to locate the real-world smart meters, we show the effectiveness of our proposed scheme based on an extensive study of two scenarios: the first is in traffic offloading in the cellular network, and the second is in IoT applications, specifically, smart grid communications.

We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the joint optimization of network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use in the DASH framework. Further, NOVA can be extended for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.

We propose a video coding and delivery scheme which is geared towards low bit-rate and real-time performance requirements. We use a finite state wavelet-based hierarchical lookup vector quantization (FSWHVQ) scheme, which embeds the optical flow calculations in table-lookups. This video coding scheme is both fast (table-lookups) and accurate (dense motion field), and avoids the blocking artifacts and poor prediction which plagues block coding schemes at low bit rates. For restricted image compression/transmission scenarios like teleconferencing, for which a good training set may be available, the FSWHVQ scheme may be viewed as storing an internal representation in its lookup tables, a valid and complete model of the problem domain.

Accompanied by the rapid development of mobile video service requirements, the dramatic increase in video streaming traffic causes a heavy burden for mobile networks. Mobile edge computing (MEC) has become a promising paradigm to enhance the mobile networks by providing cloud-computing capabilities within the radio access network (RAN). With the ability of content caching and context awareness, MEC could provide low-latency and adaptive-bitrate video streaming to improve service providing ability of the RAN. In this paper, we propose an MEC enhanced adaptive bitrate (ABR) video delivery scheme, which combines content caching and ABR streaming technology together. The MEC server acts as a controlling component to implement the video caching strategy and adjust the transmitted bitrate version of videos flexibly. A Stackelberg game is formulated to deal with the storage resources occupied by each base station. The joint cache and radio resource allocation (JCRA) is tackled into a matching problem. We propose the JCRA algorithm to solve the matching problem in order to make the cooperation between cache and radio resources. Simulation results reveal that the proposed scheme could improve both the cache hit ratio and the system throughput over other schemes.

This paper proposes an approach for the evaluation of DASH-based video transmission from a server located in a Content Delivery Network (CDN) to multiple LTE users. The approach is based on utilizing an analytical model for the HTTP/TCP transmission in the wired/core link, and simulation traces for the packet transmission in the wireless/access link. The core and access link are separately considered by using the Wireless Transmission Control Protocol (WTCP) at the eNodeB, and three scheduling approaches are used for delivering the video packets in the access link. Therefore, in our proposed approach, we emulate DASH-based end-to-end video delivery and observe how the scheduling strategy affects the video streaming quality depending on the number of users in the system.

Automated and timely detection of malfunctioning cells in Long-Term Evolution (LTE) networks is of high importance. Sleeping cell is a particular type of cell degradation hardly detectable by traditional network monitoring systems. Recent introduction of Minimization of Drive Test (MDT) functionality enables to collect user-level statistics from regular user devices without expensive and time-consuming drive-test and measurement campaigns. In this study data mining techniques are used to process MDT measurements to detect efficiently a sleeping cell. The developed earlier data mining framework is briefly overviewed in the paper. Special attention is devoted to post-processing stage as one of the key elements of the detection scheme. In practice, location information of collected measurements might contain considerable errors. This factor impacts the precision of malfunctioning cell detection. Therefore several post-processing algorithms are proposed, where location accuracy is taken into account. The performance of the algorithms is compared based on the results of thorough system-level LTE network simulations. Combined post-processing method shows the best reliability against location errors in terms of Root Mean Squared Error (RMSE) and percent gain.

In this paper, we consider the delivery of layered video from parallel heterogenous servers within a video-on-demand infrastruture. A parallel server architecture enables the service of requests by more than one server, thus reducing load at individual servers and dispersing network load. Serving requests for a single video through all or a subset of servers in the system reduces the probability of server overload brought about by a large number of requests for popular content; more clients may also be admitted for the retrieval of video data. Delivery through multiple servers requires that the video data be partitioned. Ideally, the data should be partitioned such that multiple server retrieval provides the same download and access time performance possible when retrieving from a single server of the same total bandwidth. We design and analyse play-while-retrieve strategies that involve streaming layers from different servers and show how access time can be reduced through these strategies. While system wide data striping can completely remove the problem of hotspotting, the method does not scale well and problems may be encountered when the system grow in size or when heterogenous disks have to be used. Since our proposed scheme takes into consideration heterogenous upload bandwidth and layer bitrates, it may be suitable for a peer to peer network where peer upload bandwidth is limited and varied.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Wireless self-backhaul technology is an effective solution to the backhaul bottleneck of 5G Heterogeneous Networks (HetNets). To effectively utilize the spectrum resource as well as reduce the transmission load of backhaul links, we propose a joint Context-aware Data Caching and Resource Allocation (CDCRA) scheme for self-backhauled HetNets aiming at maximizing the total system throughput. Context-aware technology combined with many-to-many matching theory is utilized for data caching in small base stations (SBSs). Then interference management based on graph theory is considered when allocating spectrum resource for both access links and backhaul links. Different comparison schemes are designed to verify the performance of our proposed scheme. Simulation results indicate that the total system throughput is significantly improved by CDCRA scheme comparing with other data caching and resource allocation schemes.

The increasing adoption of Machine Type Communication (MTC) applications using the Long Term Evolution (LTE) brings new challenges for the traditional bearer allocation and network management. MTC devices are high in number, each requiring an individual bearer for a short data transmission; an inefficient process considering the signaling and management effort to the duration of the actual bearer use. This paper introduces the notion of a virtual bearer that can be shared among a number of MTC devices with similar Quality of Service (QoS) characteristics. The virtual bearer support at the MTC device side is based on the paradigm of Network Function Virtualization (NFV) and can migrate from one MTC device to another eliminating the need for individual device bearer establishment for both the radio and backhaul. The use of virtual bearers is enhanced by considering additionally MTC device-to-device (D2D) connectivity allowing devices without direct access to the network to use the virtual bearer via other devices, which act as gateways. Gateway devices may also be re-located dynamically based on NFV principles. In this way, a finer control is achieved on prioritizing certain services and providing load balancing. Our simulation results demonstrate the benefits of our proposed scheme compared to conventional approaches wherein MTC devices use individual bearers to send data in response to a trigger or according to a predetermined schedule.

The rapid increasing demands of wireless multimedia applications have boosted the developments of video delivery technologies with cross-layer designs, driven by optimizing quality of experiences (QoEs) of end users. In this paper, a near optimal power allocation scheme, targeting at maximizing QoE, is proposed for transmitting scalable video coding (SVC) based videos over multi-input multi-output (MIMO) systems. Both transmission errors in the physical (PHY) layer and video source coding characteristics in the application (APP) layer are jointly considered in the proposed scheme. A near optimal solution is achieved by decomposing the original optimization problem into several convex optimization sub-problems. Detailed algorithms with corresponding theoretical reasoning are provided. Since forward error corrections (FEC) techniques are widely implemented in modern wireless communication systems, the proposed scheme is further extended to the systems with Reed-Solomon (RS) code and a more practical approach with different modulation and coding schemes (MCSs). The near optimality of our proposed scheme, in terms of measured utilities, is shown by comparing with the exhaustive searched optimal solutions. Simulations with real H.264 SVC video traces demonstrate the effectiveness of our proposed scheme by comparing with other existing schemes in terms of well-accepted video quality assessment methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.