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MEC-Assisted Flexible Transcoding Strategy for Adaptive Bitrate Video Streaming in Small Cell Networks

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China Communications 2021年第2期18卷 200-214页

作者： Chunyu Liu heli zhang Hong Ji Xi Li School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Universal Wireless Communications Ministry of Education(Beijing University of Posts and Telecommunications)Beijing 100876China

Adaptive bitrate video streaming(ABR)has become a critical technique for mobile video streaming to cope with time-varying network conditions and different user ***,there are still many problems in achieving high-quality ABR video streaming over cellular *** Edge Computing(MEC)is a promising paradigm to overcome the above problems by providing video transcoding capability and caching the ABR video streaming within the radio access network(RAN).In this paper,we propose a flexible transcoding strategy to provide viewers with low-latency video streaming services in the MEC networks under the limited storage,computing,and spectrum *** to the information collected from users,the MEC server acts as a controlling component to adjust the transcoding strategy flexibly based on optimizing the video caching placement ***,we cache the proper bitrate version of the video segments at the edge servers and select the appropriate bitrate version of the video segments to perform transcoding under jointly considering access control,resource allocation,and user *** formulate this problem as a nonconvex optimization and mixed combinatorial ***,the simulation results indicate that our proposed algorithm can ensure a low-latency viewing experience for users.

关键词： mobile edge computing adaptive bi-trate video streaming flexible transcoding strategy ADMM

Resource allocation and hybrid prediction scheme for low-latency visual feedbacks to support tactile Internet multimodal perceptions

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 13-28页

作者： Kang Mancong Li Xi Ji Hong zhang heli School of Telecommunication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network(DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.

关键词： tactile Internet ultra-reliable and low-latency communications enhanced mobile broadband visuo-haptic feedbacks layered rendering

Dynamic hierarchical collaborative caching scheme in ultra-dense networks

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The Journal of China Universities of Posts and Telecommunications 2021年第1期28卷 78-86页

作者： Chen Jianing Li Xi Ji Hong zhang heli Key Laboralory of Univensal Wireless Communications Bejing University of Posts and TelecommunieationsBejing 100876China School of Information and Communieation Engineering Beijing Universily of Posts and TelecommunicationsBeijing 100876China

Pushing popular contents to the edge of the network can meet the growing demand for data traffic, reduce latency and relieve the pressure of the backhaul. However, considering the limited storage space of the base stations, it is impossible to cache all the contents, especially in ultra-dense network(UDN). Furthermore, the uneven distribution of mobile users results in load imbalance among small base stations(SBSs) in both time and space, which also affects the caching strategy. To overcome these shortcoming, the impact of the changing load imbalance in UDN was investigated, and then a dynamic hierarchical collaborative caching(DHCC) scheme was proposed to optimize latency and caching hit rate. The storage of the SBS is logically divided into the independent caching layer and the collaborative caching layer. The independent caching layer caches the most popular contents for local users’ interest, and the collaborative caching layer caches contents as much as possible for the benefit of content diversity in the region. Different SBSs have respective storage space layer division ratios, according to their real-time traffic load. For SBSs with heavy load, the independent caching layers are allocated with more space. Otherwise, the collaborative caching layers could store more contents with larger space. The simulation results show that, DHCC improved both transmission latency and hit rate compared with existing caching schemes.

关键词： ultra-dense network(UDN) load imbalance caching

QoE-based video segments caching strategy in urban public transportation system

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The Journal of China Universities of Posts and Telecommunications 2021年第4期28卷 29-38页

作者： Wang Hang Li Xi Ji Hong zhang heli School of Telecommunication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China

With the rapid development of vehicle-based applications, entertainment videos have gained popularity for passengers on public vehicles. Therefore, how to provide high quality video service for passengers in typical public transportation scenarios is an essential problem. This paper proposes a quality of experience(QoE)-based video segments caching(QoE-VSC) strategy to guarantee the smooth watching experience of passengers. Consequently, this paper considers a jointly caching scenario where the bus provides the beginning segments of a video, and the road side unit(RSU) offers the remaining for passengers. To evaluate the effectiveness, QoE hit ratio is defined to represent the probability that the bus and RSUs jointly provide passengers with desirable video segments successfully. Furthermore, since passenger volume change will lead to different video preferences, a deep reinforcement learning(DRL) network is trained to generate the segment replacing policy on the video segments cached by the bus server. And the training target of DRL is to maximize the QoE hit ratio, thus enabling more passengers to get the required video. The simulation results prove that the proposed method has a better performance than baseline methods in terms of QoE hit ratio and cache costs.

关键词： caching strategy quality of experience deep reinforcement learning urban public transportation system

Distributed Edge Cooperation and Data Collection for Digital Twins of Wide-Areas

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China Communications 2023年第8期20卷 177-197页

作者： Mancong Kang Xi Li Hong Ji heli zhang Key Laboratory of Universal Wireless Communications Beijing University of Posts and TelecommunicationsBeijing 100876China

Digital twins for wide-areas(DT-WA)can model and predict the physical world with high fidelity by incorporating an artificial intelligence(AI)***,the AI model requires an energy-consuming updating process to keep pace with the dynamic environment,where studies are still in *** reduce the updating energy,this paper proposes a distributed edge cooperation and data collection *** AI model is partitioned into multiple sub-models deployed on different edge servers(ESs)co-located with access points across wide-area,to update distributively using local sensor *** reduce the updating energy,ESs can choose to become either updating helpers or recipients of their neighboring ESs,based on sensor quantities and basic updating *** would share their updated sub-model parameters with neighboring recipients,so as to reduce the latter updating *** minimize system energy under updating convergency and latency constraints,we further propose an algorithm to let ESs distributively optimize their cooperation identities,collect sensor data,and allocate wireless and computing *** comprises several constraint-release approaches,where two child optimization problems are solved,and designs a largescale multi-agent deep reinforcement learning *** shows that the proposed scheme can efficiently reduce updating energy compared with the baselines.

关键词： digital twin smart city multi-agent deep reinforcement learning resource allocation

Efficient caching strategy based on graph coloring for moving users in ultra dense networks

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The Journal of China Universities of Posts and Telecommunications 2019年第3期26卷 1-7页

作者： Hu Chengjia Li Xi Ji Hong zhang heli Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications

Caching popular files in small-cell base stations(SBSs) is considered as a promising technique to meet the demand of ever growing mobile data traffic in ultra dense networks(UDNs). Considering the limited cache capacity and dense deployment of SBSs, how to support uninterrupted and successful caching downloading for moving users is still a challenging problem. In this paper, a graph-coloring-based caching(GCC) algorithm in UDN for moving user under limited SBS storage capacities is proposed. Firstly, considering there may be downloading interruption or even failure due to the random moving of users and small coverage of SBSs, graph coloring algorithm(GCA) is employed for grouping the SBS to cache fragments of several files. Then, the problem of how to conduct caching placement on SBSs is formulated aiming to maximize the amount of data downloaded from SBSs. Finally, an efficient heuristic solution is proposed to get an optimal result. Simulation results show that the algorithm performs better than other caching strategies in prior work, in terms of reducing both backhaul traffic and user download delay.

关键词： ultra dense network graph coloring caching moving user download delay

Hierarchical mobility prediction in the future heterogeneous networks for the tradeoff between accuracy and complexity

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The Journal of China Universities of Posts and Telecommunications 2019年第3期26卷 15-24页

作者： Liu Zhenya Li Xi Ji Hong zhang heli Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing 100876 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing 100876 China

Mobility prediction is one of the promising technologies for improving quality of service(QoS) and network resource utilization. In future heterogeneous networks(HetNets), the network topology will become extremely complicated due to the widespread deployment of different types of small-cell base stations(SBSs). For this complex network topology, traditional mobility prediction methods may cost unacceptable overhead to maintain high prediction accuracy. This problem is studied in this paper, and the hierarchical mobility prediction scheme(HMPS) is proposed for the future HetNets. By dividing the entire process into two prediction stages with different granularity, the tradeoff between prediction accuracy and computational complexity is investigated. Before performing prediction of user mobility, some frequently visited locations are identified from the user’s trajectory, and each location represents an important geographic area(IGA). In the coarse-grained prediction phase, the next most possible location to be visited is predicted at the level of the possible geographic areas by using a second-order Markov chain with fallback. Then, the fine-grained prediction of user position is performed based on hidden Markov model(HMM) from temporal and spacial dimensions. Simulation results demonstrate that, compared with the existing prediction methods, the proposed HMPS can achieve a good compromise between prediction accuracy and complexity.

关键词： mobility prediction heterogeneous networks prediction accuracy and complexity Markov chain, HMM

Resource allocation scheme based on game theory in heterogeneous networks

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The Journal of China Universities of Posts and Telecommunications 2016年第3期23卷 57-62,88页

作者： Wu Yalong Li Xi zhang heli Wang Ke Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications

As an effective solution for indoor coverage and service offioading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. This study investigates the resource block （RB） and power allocation in heterogeneous networks （HetNets）. Specifically, the concern here is to maximize the signal to interference-plus-noise ratio （SINR） of macrocell and energy efficiency of femtocell while providing the finite interference. In this paper, the system model is divided to two layers, in which the macro base station and clusters constitute the first layer network; femtocells in cluster make up the second layer network. Because of the different model structures, different game theories are used in different layers. Stackelberg game is used in the first layer, and non-cooperation game is used in the second layer. Meanwhile RB and power levels stand for the actions that are associated with each player in the game. The problem of resource allocation is formulated as a mixed integer programming problem. In order to minimize the complexity of the proposed algorithm, the resource allocation task is decomposed into two sub problems： a RB allocation and a power allocation. The result is compared with the traditional methods, the analysis illustrates the proposed algorithm has a better performance regarding SINR and energy efficiency of the heterogeneous networks.

关键词： game theory resource block allocation power allocation

Blockchain-based collaborative edge caching scheme for trustworthy content sharing

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 38-47页

作者： Zhou Yutong Li Xi Ji Hong zhang heli Key Laboratory of Universal Wireless Communications Beijing University of Posts and TelecommunicationsBeijing 100876China

Moving data from cloud to the edge network can effectively reduce traffic burden on the core network,and edge collaboration can further improve the edge caching capacity and the quality of service(QoS).However,it is difficult for various edge caching devices to cooperate due to the lack of trust and the existence of malicious *** this paper,blockchain which has the distributed and immutable characteristics is utilized to build a trustworthy collaborative edge caching scheme to make full use of the storage resources of various edge *** collaboration process is described in this paper,and a proof of credit(PoC)protocol is proposed,in which credit and tokens are used to encourage nodes to cache and transmit more content in honest *** nodes will pay for their malicious actions such as tampering or deleting cached *** each node chooses strategy independently to maximize its benefits in an environment of mutual influence,a non-cooperative game model is designed to study the caching behavior among edge *** existence of Nash equilibrium(NE)is proved in this game,so the edge server(ES)can choose the optimal caching strategy for all collaborative devices,including itself,to obtain the maximum *** results show that the system can save mining overhead as well as organize a trusted collaborative edge caching effectively.

关键词： caching blockchain collaborative edges consensus mechanism game theory