网络中的资源分配问题一直备受关注,特别是在超高清视频流的传输中,对资源的有效管理至关重要。然而,随着网络服务的多样化和不断增加的业务类型,传统的资源分配策略往往显得不够灵活和智能。深度Q网络(Deep Q-Network,DQN)是一种能够...网络中的资源分配问题一直备受关注,特别是在超高清视频流的传输中,对资源的有效管理至关重要。然而,随着网络服务的多样化和不断增加的业务类型,传统的资源分配策略往往显得不够灵活和智能。深度Q网络(Deep Q-Network,DQN)是一种能够自适应地学习和调整资源分配策略的神经网络模型。它基于神经网络与Q-Learning算法,通过不断尝试和学习来决策最佳的资源分配方案。本文旨在研究一种在云演艺场景下基于深度Q网络的延迟敏感业务资源调度算法,以满足当今网络中多样化的业务需求。仿真结果表明,基于深度Q网络的延迟敏感业务资源调度算法使得用户体验质量(Quality of Experience)指标显著提升,表明所提算法能够更好地满足延迟敏感业务的需求。展开更多
面向低时延、稳定传输、高用户体验质量(quality of experience,QoE)的网络实时传输需求场景,提出一种低时延智能网络数据传输调度算法。该算法由数据块排队控制策略和拥塞控制策略两部分组成。数据排队控制策略提出了综合数据块的创建...面向低时延、稳定传输、高用户体验质量(quality of experience,QoE)的网络实时传输需求场景,提出一种低时延智能网络数据传输调度算法。该算法由数据块排队控制策略和拥塞控制策略两部分组成。数据排队控制策略提出了综合数据块的创建时间和有效时限(effective time)的性价比模型,有效地解决了传输时间约束下的信息传输不均衡问题;拥塞控制策略提出了基于使用耿贝尔分布(Gumbel distribution)采样重参数化与混合经验优先级模型改进后的深度确定性策略梯度(deep deterministic policy gradient,DDPG)方法,解决了深度确定性策略梯度不适用于离散网络动作空间拥塞控制的问题,并通过学习自适应调整发送参数显著提升了网络拥塞控制质量。实验结果表明,实时传输场景下使用本文提出的排队算法能够有效提升QoE,采用改进后的DDPG进行拥塞控制能大幅降低传输时延。同样场景下,将提出的智能网络数据传输调度算法与排队策略及拥塞控制策略相结合,与传统的网络数据传输调度算法相比,能够更好地兼顾低时延和稳定传输,提供更高的数据传输质量。展开更多
Existing systems use key performance indicators(KPIs)as metrics for physical layer(PHY)optimization,which suffers from the problem of overoptimization,because some unnecessary PHY enhancements are imperceptible to ter...Existing systems use key performance indicators(KPIs)as metrics for physical layer(PHY)optimization,which suffers from the problem of overoptimization,because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste.Therefore,it is necessary to utilize directly the quality of experience(QoE)of user as a metric of optimization,which can achieve the global optimum of QoE under cost and energy constraints.However,QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY.To address this problem,we in this paper propose a novel end-to-end QoE(E2E-QoE)based optimization architecture at the user-side for the first time.Specifically,a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE.Based on this,an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies.Finally,we investigate to optimize the PHY algorithm directly based on the E2E-QoE.The proposed frameworks and algorithms are all validated using the data from real fifth-generation(5G)mobile system,which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.展开更多
In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned...In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.展开更多
利用比例积分微分(PID)控制理论控制IP语音(VoIP)数据流带宽需求和用户体验(QoE)之间的平衡关系,并将该信息纳入带宽再分配模型中,提出一个基于QoE的带宽分配机制QBAV(QoE-based bandwidth allocation mechanism for VoIP application)...利用比例积分微分(PID)控制理论控制IP语音(VoIP)数据流带宽需求和用户体验(QoE)之间的平衡关系,并将该信息纳入带宽再分配模型中,提出一个基于QoE的带宽分配机制QBAV(QoE-based bandwidth allocation mechanism for VoIP application),它在满足用户期望QoE的同时兼顾带宽分配的公平性。理论证明该算法满足非线性问题最优化约束条件,所分配带宽满足目标函数全局收敛。仿真结果表明,该算法分别以低于传统NRG算法9%及最新FC-MDI-S算法15%的带宽满足了90%以上用户的期望QoE,避免了现有带宽分配机制针对小部分高优先级数据流的弊端,提高了VoIP业务的整体性能及网络利用效率。展开更多
移动视频业务应用广泛,流量占比高且持续增长.针对有限的移动网络带宽,如何合理地规划网络服务、提供优质的移动视频体验,需要客观的视频体验评估反馈网络服务提供商和视频服务运营商以改善网络利用率及传输方案.当前大多数视频服务质...移动视频业务应用广泛,流量占比高且持续增长.针对有限的移动网络带宽,如何合理地规划网络服务、提供优质的移动视频体验,需要客观的视频体验评估反馈网络服务提供商和视频服务运营商以改善网络利用率及传输方案.当前大多数视频服务质量评估方法都基于DPI(Deep Packet Inspection)方法获取视频播放信息以计算视频QoE(Quality of Experience).然而,为了保护用户隐私和网络安全,越来越多的视频采用HTTPS加密传输,使得传统的DPI方法无法获取码率和清晰度等QoE评估参数.因此,文中提出一种基于视频块统计特征的加密视频QoE参数识别方法(以代表性网络视频YouTube为例).首先,根据SSL/TLS协议握手过程中未加密部分识别HTTPS加密的YouTube流量.然后,根据视频流前若干个包的4种特征识别出HLS、DASH和HPD传输模式,再根据视频块统计特征建立机器学习模式识别视频块的码率和清晰度.实验结果表明该方法传输模式、码率和清晰度识别平均准确率分别达到98%、99%和98%,可以有效用于加密YouTube的QoE评估.展开更多
HAS(HTTP Adaptive Streaming)能够实现流畅播放和视频质量的平衡,为用户提供更好的服务质量体验。大多数基于HAS的流媒体用户体验质量(Quality of Experience,QoE)模型考虑了当前系统或网络条件,但对用户所处环境的客观影响、用户心理...HAS(HTTP Adaptive Streaming)能够实现流畅播放和视频质量的平衡,为用户提供更好的服务质量体验。大多数基于HAS的流媒体用户体验质量(Quality of Experience,QoE)模型考虑了当前系统或网络条件,但对用户所处环境的客观影响、用户心理因素的考虑较少。面向移动流媒体客户端的应用场景,从客观感知影响参数和心理效应影响参数两个方面来考虑移动端流媒体的QoE影响因素,设计用户QoE评估模型。提出移动设备抖动状态检测和用户观看位置检测方法,并将设备抖动状态、用户观看位置与流媒体服务质量相结合,再根据心理学系列位置效应来综合评估用户的质量体验情况。最后通过实验证明了所提的用户QoE模型能够提供准确有效且符合用户实际体验的QoE评估结果。展开更多
为优化未来多层无线网络覆盖中视频业务的体验质量(quality of experience,QoE),在基于生物信息学的细胞吸引子选择模型上研究多维吸引子选择算法,对每个接入网视频业务的吸引力进行建模,设置参数控制吸引子吸引力度和算法收敛速度,当Qo...为优化未来多层无线网络覆盖中视频业务的体验质量(quality of experience,QoE),在基于生物信息学的细胞吸引子选择模型上研究多维吸引子选择算法,对每个接入网视频业务的吸引力进行建模,设置参数控制吸引子吸引力度和算法收敛速度,当QoE低于用户容忍阈值时,该模型会根据当前QoE值重新计算各个接入网应分得的视频流量,使当前视频QoE值重新达到用户要求。仿真结果表明,通过持续的反馈-调整闭环机制,使该方法在网络变差时优化视频业务QoE。展开更多
以系统平均用户感受质量QoE(Quality of Experience)为优化目标,提出一种基于QoE效用函数的子载波和功率分配算法。该算法先根据用户需求与实际信道状态对子载波进行分配,将QoE引入效用函数构建中,并以系统平均QoE最大化为目标对子载波...以系统平均用户感受质量QoE(Quality of Experience)为优化目标,提出一种基于QoE效用函数的子载波和功率分配算法。该算法先根据用户需求与实际信道状态对子载波进行分配,将QoE引入效用函数构建中,并以系统平均QoE最大化为目标对子载波进行功率分配,在功率分配过程中使用导数迭代的方法逼近最优的功率分配结果。仿真结果表明,该算法在获得较高系统平均QoE的同时,根据系统功率资源供求情况,平衡考虑了系统、容量以及用户公平性。展开更多
文摘网络中的资源分配问题一直备受关注,特别是在超高清视频流的传输中,对资源的有效管理至关重要。然而,随着网络服务的多样化和不断增加的业务类型,传统的资源分配策略往往显得不够灵活和智能。深度Q网络(Deep Q-Network,DQN)是一种能够自适应地学习和调整资源分配策略的神经网络模型。它基于神经网络与Q-Learning算法,通过不断尝试和学习来决策最佳的资源分配方案。本文旨在研究一种在云演艺场景下基于深度Q网络的延迟敏感业务资源调度算法,以满足当今网络中多样化的业务需求。仿真结果表明,基于深度Q网络的延迟敏感业务资源调度算法使得用户体验质量(Quality of Experience)指标显著提升,表明所提算法能够更好地满足延迟敏感业务的需求。
文摘Existing systems use key performance indicators(KPIs)as metrics for physical layer(PHY)optimization,which suffers from the problem of overoptimization,because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste.Therefore,it is necessary to utilize directly the quality of experience(QoE)of user as a metric of optimization,which can achieve the global optimum of QoE under cost and energy constraints.However,QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY.To address this problem,we in this paper propose a novel end-to-end QoE(E2E-QoE)based optimization architecture at the user-side for the first time.Specifically,a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE.Based on this,an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies.Finally,we investigate to optimize the PHY algorithm directly based on the E2E-QoE.The proposed frameworks and algorithms are all validated using the data from real fifth-generation(5G)mobile system,which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.
文摘In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.
文摘利用比例积分微分(PID)控制理论控制IP语音(VoIP)数据流带宽需求和用户体验(QoE)之间的平衡关系,并将该信息纳入带宽再分配模型中,提出一个基于QoE的带宽分配机制QBAV(QoE-based bandwidth allocation mechanism for VoIP application),它在满足用户期望QoE的同时兼顾带宽分配的公平性。理论证明该算法满足非线性问题最优化约束条件,所分配带宽满足目标函数全局收敛。仿真结果表明,该算法分别以低于传统NRG算法9%及最新FC-MDI-S算法15%的带宽满足了90%以上用户的期望QoE,避免了现有带宽分配机制针对小部分高优先级数据流的弊端,提高了VoIP业务的整体性能及网络利用效率。
文摘移动视频业务应用广泛,流量占比高且持续增长.针对有限的移动网络带宽,如何合理地规划网络服务、提供优质的移动视频体验,需要客观的视频体验评估反馈网络服务提供商和视频服务运营商以改善网络利用率及传输方案.当前大多数视频服务质量评估方法都基于DPI(Deep Packet Inspection)方法获取视频播放信息以计算视频QoE(Quality of Experience).然而,为了保护用户隐私和网络安全,越来越多的视频采用HTTPS加密传输,使得传统的DPI方法无法获取码率和清晰度等QoE评估参数.因此,文中提出一种基于视频块统计特征的加密视频QoE参数识别方法(以代表性网络视频YouTube为例).首先,根据SSL/TLS协议握手过程中未加密部分识别HTTPS加密的YouTube流量.然后,根据视频流前若干个包的4种特征识别出HLS、DASH和HPD传输模式,再根据视频块统计特征建立机器学习模式识别视频块的码率和清晰度.实验结果表明该方法传输模式、码率和清晰度识别平均准确率分别达到98%、99%和98%,可以有效用于加密YouTube的QoE评估.
文摘HAS(HTTP Adaptive Streaming)能够实现流畅播放和视频质量的平衡,为用户提供更好的服务质量体验。大多数基于HAS的流媒体用户体验质量(Quality of Experience,QoE)模型考虑了当前系统或网络条件,但对用户所处环境的客观影响、用户心理因素的考虑较少。面向移动流媒体客户端的应用场景,从客观感知影响参数和心理效应影响参数两个方面来考虑移动端流媒体的QoE影响因素,设计用户QoE评估模型。提出移动设备抖动状态检测和用户观看位置检测方法,并将设备抖动状态、用户观看位置与流媒体服务质量相结合,再根据心理学系列位置效应来综合评估用户的质量体验情况。最后通过实验证明了所提的用户QoE模型能够提供准确有效且符合用户实际体验的QoE评估结果。
文摘为优化未来多层无线网络覆盖中视频业务的体验质量(quality of experience,QoE),在基于生物信息学的细胞吸引子选择模型上研究多维吸引子选择算法,对每个接入网视频业务的吸引力进行建模,设置参数控制吸引子吸引力度和算法收敛速度,当QoE低于用户容忍阈值时,该模型会根据当前QoE值重新计算各个接入网应分得的视频流量,使当前视频QoE值重新达到用户要求。仿真结果表明,通过持续的反馈-调整闭环机制,使该方法在网络变差时优化视频业务QoE。
文摘以系统平均用户感受质量QoE(Quality of Experience)为优化目标,提出一种基于QoE效用函数的子载波和功率分配算法。该算法先根据用户需求与实际信道状态对子载波进行分配,将QoE引入效用函数构建中,并以系统平均QoE最大化为目标对子载波进行功率分配,在功率分配过程中使用导数迭代的方法逼近最优的功率分配结果。仿真结果表明,该算法在获得较高系统平均QoE的同时,根据系统功率资源供求情况,平衡考虑了系统、容量以及用户公平性。
