Average Quality Of Experience Research Articles

Artificial Intelligence Workload Allocation Method for Vehicular Edge Computing

Real-time applications such as smart transportation systems require minimum response time to increase performance. Incorporating edge computing, processing units near end devices, achieving fast response time. The collaboration between edge servers and cloud servers is beneficial in achieving the lowest response time by using edge servers and high computational resources by using cloud servers. The workload allocation between edge–cloud servers is challenging, especially in a highly dynamic system with multiple factors varying over time. In this paper, the workload allocation decisions among the edge servers and cloud are considered for autonomous vehicle systems. The autonomous vehicle system generates multiple tasks belonging to different AI applications running on the vehicles. The proposed method considers allocating the tasks to edge or cloud servers. The cloud servers can be reached through a cellular network or a wireless network. The proposed method is based on designing a neural network model and using a high number of features that contribute to the decision-making process. A huge dataset has also been generated for the implementation. The EdgeCloudSim is used as a simulator for implementation. The competitor's methods considered for the comparison are random, simple moving average (SMA) based, multi-armed bandit (MAB) theory-based, game theory-based, and machine learning-based workload allocation methods. The result shows an improvement in the average Quality of Experience (QoE), ranging from 8.33% to 28.57%, while the average failure rate achieved enhancement up to 50%.

Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile Environments

The rapid growth of mobile video traffic and user demand poses a more stringent requirement for efficient bandwidth allocation in mobile networks where multiple users may share a bottleneck link. This provides content providers an opportunity to jointly optimize multiple users’ experiences but users often suffer short connection durations and frequent handoffs because of their high mobility. In this paper, we propose an end-to-end scheme, VSiM, for supporting mobile video streaming applications in heterogeneous wireless networks. The key idea is allocating bottleneck bandwidth among multiple users based on their mobility profiles and Quality of Experience (QoE)-related knowledge to achieve max-min QoE fairness. Besides, the QoE of buffer-sensitive clients is further improved by the novel server push strategy based on HTTP/3 protocol without affecting the existing bandwidth allocation approach or sacrificing other clients’ view quality. VSiM is lightweight and easy to deploy in the real world without touching the underlying network infrastructure. We evaluated VSiM experimentally in both simulations and a lab testbed on top of the HTTP/3 protocol. We find that the clients’ QoE fairness of VSiM achieves more than 40% improvement compared with state-of-the-art solutions, i.e., the viewing quality of clients in VSiM can be improved from 720p to 1080p in resolution. Meanwhile, VSiM provides about 20% improvement of average QoE.

QoE-aware budgeted edge data caching online: A primal–dual approach

Data caching has garnered significant attention in the field of edge computing due to the low-latency services offered by nearby edge servers. To efficiently utilize the data cache budget and ensure minimal data transmission latency, selecting appropriate edge servers for data caching is crucial. While the users’ Quality of Experience (QoE) plays a vital role in the service providers’ benefits, it has not been adequately considered in dynamic edge computing environments. This is primarily due to the non-linear correlation between QoE and Quality-of-Service (QoS), making cost-effective edge data caching challenging within the service provider’s limited budget. In this paper, we formally define the QoE-aware budgeted online edge data caching (QoE-BOEDC) problem, aiming to maximize the average user QoE while adhering to the service provider’s budget in a dynamic edge computing environment. Here, we formulate a formal optimization model for QoE-BOEDC, which can be proven to be NP-complete. Subsequently, we propose an online algorithm, PDOQ (Primal–Dual Optimization for QoE), based on the primal–dual technique, and theoretically establish its comparative ratio. Additionally, we conduct both small-scale and large-scale experimental tests, and the experimental results demonstrate that PDOQ significantly outperforms other representative algorithms.

COCKTAIL: Video streaming QoE optimization with chunk replacement and guided learning

Adaptive bitrate (ABR) algorithms aim to provide the best user quality of experience (QoE) under fluctuating operating environments. Prior ABR protocols address the QoE maximization problem with a plethora of approximate optimization techniques including model predictive control (MPC), Lyapunov optimization, and deep reinforcement learning (DRL). Even though these algorithms provide adequate performances, they focus only on bitrate selections, precluding the chunk replacement option. We proclaim that chunk replacement can enhance the QoE if duplicated downloading is carefully administered. Moreover, we point out that bitrate selection and chunk replacement should be closely coupled into the optimization problem to fully realize the potential of chunk replacement. We first formulate a novel optimization problem with an expanded decision space that encompasses chunk replacement as well as bitrate selection. We then propose COCKTAIL, a DRL-based ABR algorithm that discovers efficient solutions to the new optimization problem by using several learning techniques. Experiments on real-world network traces show that COCKTAIL outperforms state-of-the-art baselines with improvements up to 16.1% in average QoE.

MADRL-Based Joint Edge Caching and Bitrate Selection for Multicategory 360° Video Streaming

360-degree video streaming has gained increasing attraction in the current popular VR, AR, and MR applications, which can provide users with an immersive experience. In tile-based 360-degree video streaming, edge caching and bitrate selection strategies are jointly designed to improve users’ Quality of Experience (QoE), which incorporates video quality and rebuffer. However, the existing QoE-driven approaches use a unified QoE function to guide the decisions of edge caching and bitrate selection, which neglect the impact of video quality and rebuffer on different categories of 360-degree videos, thus failing to provide high average QoE for users. In this paper, we propose a MADRL-based joint edge caching and bitrate selection strategy for multicategory 360-degree video streaming to improve users’ average QoE. The key idea is to employ different edge caching and bitrate selection strategies for different video categories to enable fine-grained performance optimization. Based on multicategory 360-degree video streaming, we first model a joint edge caching and bitrate selection problem as a multi-agent cooperative Markov decision process with the goal of maximizing users’ average QoE. Next, an FoV-aware multi-agent soft actor-critic (FA-MASAC) algorithm is designed to help agents collaboratively learn optimal edge caching and bitrate selection decisions in a distributed way, in which each video category is treated as an agent. Finally, experimental results on real-world datasets show that our proposed strategy can greatly benefit users’ average QoE compared to existing strategies.

A Visual Sensitivity Aware ABR Algorithm for DASH via Deep Reinforcement Learning

In order to cope with the fluctuation of network bandwidth and provide smooth video services, adaptive video streaming technology is proposed. In particular, the adaptive bitrate (ABR) algorithm is widely used in dynamic adaptive streaming over HTTP (DASH) to improve quality of experience (QoE). However, existing ABR algorithms still ignore the inherent visual sensitivity of human visual system (HVS). As the final receiver of video, HVS has different sensitivity to the quality distortion of different video content, and video content with high visual sensitivity needs to allocate more bitrate resources. Therefore, existing ABR algorithms still have limitations in reasonably allocating bitrate and maximizing QoE. To solve this problem, this paper designs an adaptive bitrate strategy from the perspective of user vision, studies the modeling of visual sensitivity, and proposes a visual sensitivity aware ABR algorithm. We extract a set of content features and attribute features from the video, and consider the simulation of HVS to establish a total masking effect model that reflects the visual sensitivity more accurately. Further, the network status, buffer occupancy, and visual sensitivity are comprehensively considered under a deep reinforcement learning framework to select the appropriate bitrate for maximizing QoE. We implement the proposed algorithm over a realistic trace-driven evaluation and compare its performance with several latest algorithms. Experimental results show that our algorithm can align ABR strategy with visual sensitivity to achieve better QoE in high visual sensitivity content, and improves the average perceptual video quality and overall user QoE by 18.3% and 22.8%, respectively. Additionally, we prove the feasibility of our algorithm through subjective evaluation in the real environment.

Container Scheduling in Co-Located Environments Using QoE Awareness

Existing Cloud deployments usually perform automated scheduling and rescheduling based on Quality of Service (QoS) objectives. Services are migrating towards Quality of Experience (QoE), which maps the user experience more effectively than QoS. This work proposes extensions to the Kubernetes scheduler in order to employ QoE objectives into the algorithm. For that, we created deep learning models (using LSTM) to estimate user’s QoE that the cloud can offer. The evaluation was performed on a testbed, and considered two QoE-aware applications (live classroom and video on demand). Experimental results in a testbed show that our scheduler improves the average QoE by at least 61.5% compared to other schedulers, while our proposed resource rescheduling improved the QoE by up to 119%, keeping the average QoE closer to the maximum.

Fleet: Improving Quality of Experience for Low-Latency Live Video Streaming

Low-latency live video streaming proposes more challenges on designing Adaptive Bit Rate (ABR) algorithms than video-on-demand streaming. In this paper, we present Fleet, a solution that improves user quality of experience (QoE) in terms of video quality, video switches, video freezes, and latency. Fleet is designed to meet two operational challenges at the same time: (1) optimizing QoE under dynamic mobile network conditions and (2) ensuring a low latency experience with minimal visual quality degradation. The core of Fleet is a stochastic model predictive controller that incorporates network conditions and client states for bitrate adaptation. The idle period problem presents great challenges for bandwidth measurement and client state prediction. Fleet consists of an HTTP chunk level bandwidth measurement algorithm, and a practical live video streaming evolution model for client state prediction. Besides, a throughput probability predictor is trained to capture the mobile network’s uncertainty. And, a triple threshold playback speed controller is designed for latency management. Fleet is practically implemented in dash.js and evaluated over both synthetic and real-world mobile network conditions. Our study shows that compared with the state-of-the-art solution, Fleet achieves an average QoE improvement of 39.7% and 37.9%, respectively. Moreover, Fleet has a good generalization, outperforming existing algorithms even on new video traces and network conditions.

Content-aware QoE optimization in MEC-assisted Mobile video streaming

The traditional client-based HTTP adaptation strategies do not explicitly coordinate between the clients, servers, and cellular networks. A lack of coordination leads to suboptimal user experience. In addition to optimizing Quality of Experience (QoE), other challenges in adapting HTTP adaptive streaming (HAS) to the cellular environment are overcoming unfair allocation of the video rate and inefficient utilization of the bandwidth under the high-dynamics cellular links. Furthermore, the majority of the adaptive strategies ignore important video content characteristics and HAS client information, such as segment duration, buffer size, and video duration, in the video quality selection process. In this paper, we present a content-aware hybrid multi-access edge computing (MEC)-assisted quality adaptation algorithm by taking advantage of the capabilities of edge cloud computing. The proposed algorithm exploits video content characteristics, HAS client settings, and application-layer information to jointly adapt the bitrates of multiple clients. We design separate strategies to optimize the performance of short and long duration videos. We then demonstrate the efficiency of our algorithm against client-based solutions as well as MEC-assisted algorithms. The proposed algorithm guarantees high QoE, equitably selects video rates for clients, and efficiently utilizes the bandwidth for both short and long duration videos. The results from our extensive experiments reveal that the proposed long video adaptation algorithm outperforms state-of-the-art algorithms, with improvements in average video rate, QoE, fairness, and bandwidth utilization of 0.4%–12.3%, 8%–65%, 3.3%–5.7%, and 60%–130%, respectively. Furthermore, when high bandwidth is available to competing clients, the proposed short video adaptation algorithm improves QoE by 11.1% compared to the long video adaptation algorithm.

Live360: Viewport-Aware Transmission Optimization in Live 360-Degree Video Streaming

Live 360-degree video streaming is emerging as the next disruptive innovation in the coming era. However, compared to conventional live video streaming, it is much more challenging to provide high-quality live 360-degree video streaming due to high bandwidth demand, stringent latency requirement, and diverse user viewports. In this paper, we propose a viewport-aware live 360-degree video streaming framework called Live360 to optimize end-to-end video stream transmission. To make the problem tractable, we decompose the transmission optimization problem into two sub-problems, and optimize upstream and downstream transmission in an asynchronous way. We prioritize 360-degree cameras according to users’ real-time viewing interests and upload attractive contents with a higher bitrate. We also re-define the metric of QoE (Quality-of-Experience) for live 360-degree video viewers, and solve the optimization problems efficiently using dynamic programming. Extensive experiments show that Live360 is lightweight and significantly outperforms other state-of-the-art methods in terms of various QoE metrics. The average QoE of Live360 is twice that of other baseline methods.

PreSR: Neural-Enhanced Adaptive Streaming of VBR-Encoded Videos With Selective Prefetching

Variable Bitrate (VBR) video encoding can significantly improve the quality-of-experience (QoE) of viewing users due to its capability to provide much higher quality-to-bits ratio compared to Constant Bitrate (CBR) video encoding. However, the streaming of VBR-encoded videos suffers from large variance of video chunk size, which may directly result in frequent rebuffering if not properly handled. In this paper, we propose a novel neural-enhanced adaptive streaming framework for VBR-encoded videos called PreSR, which performs selective prefetching of video chunks to achieve a higher QoE for viewers. The design of PreSR is motivated by an important observation obtained from our measurement, namely, the video quality improvement and bandwidth savings brought by neural enhancement are more pronounced for low-resolution video chunks with complex scenes. By taking the above fact and the time required for neural enhancement into account, we formulate the problem into an optimization problem. Given that the problem is NP-hard, we design the PreSR framework, which is based on the model predictive control theory and also considers key features of VBR-encoded videos. PreSR parallelizes the download of video chunks and model inference processes to fully utilize the available compute resources. Finally, we conduct extensive experiments with real traces, and the results show that PreSR outperforms the state-of-the-art algorithms with an improvement up to 11.25% in terms of the average QoE.

Content Caching Based on Popularity and Priority of Content Using seq2seq LSTM in ICN

Efficient content caching is essential to address the explosive growth of multimedia contents and most works on content cache placement have been proposed mainly based on the popularity of content. Since the priority of content is also an important attribute of content, we consider both popularity and priority of content together for content caching in information-centric networking (ICN). We define weighted delivery cost of content as content delivery cost multiplied by a weighted sum of popularity and priority of the content. Then, we formulate optimized content cache placement problem to minimize weighted content delivery cost for all content requests in a hierarchical network architecture with multi-access edge computing (MEC) and software-defined networking (SDN) controller. Average quality of experience (QoE), i.e., average content delivery cost, for contents with each priority is imposed as constraint. The number of content requests is predicted based on seq2seq long short-term memory (LSTM) model in MECs and this is delivered to SDN controller. Then, SDN controller obtains predicted popularity of contents and decides content placement in MECs and core routers by solving the content cache placement optimization problem based on binary particle swarm optimization (BPSO). Performance of the proposed content caching scheme is compared with conventional popularity-based, popularity prediction-based, and popularity prediction-based optimization schemes, from the aspect of QoE satisfaction ratio, average cost, weighted average cost, total cost, and weighted total cost. Numerical results show the effectiveness of the proposed scheme at caching content with high priority efficiently, at the expense of caching content with low priority.

Deep Reinforcement Learning Driven UAV-Assisted Edge Computing

Unmanned aerial vehicles (UAVs) are playing a critical role in provisioning instant connectivity and computational needs of Internet of Things Devices (IoTDs), especially in crisis and disaster management. In this work, we focus on optimizing trajectories of UAVs along which IoTDs are served with communication and computing resources in multiple time slots. The Quality of Experience (QoE) of an IoTD depends on its latency performance; we thus aim to maximize the average aggregate QoE of all IoTDs overall time slots. However, this is a nonconvex, nonlinear, and mixed discrete optimization problem, which is difficult to solve and obtain the optimal solution. We thus propose two deep reinforcement learning algorithms to solve this problem by considering UAV path planning, user assignment, bandwidth, and computing resource assignment. We compare the performance of our proposed algorithms through simulations with three baseline cases: 1) with fixed UAV locations; 2) without UAVs; and 3) the fixed UAV trajectories. We demonstrate that the deep reinforcement learning algorithms perform better than all baseline cases.

Interacting With New York City Data by HoloLens Through Remote Rendering

In the digital era, Extended Reality (XR) is considered the next frontier. However, XR systems are computationally intensive, and they must be implemented within strict latency constraints. Thus, XR devices with finite computing resources are limited in terms of quality of experience (QoE) they can offer, particularly in cases of big 3D data. This problem can be effectively addressed by offloading the highly intensive rendering tasks to a remote server. Therefore, we proposed a remote rendering enabled XR system that presents the 3D city model of New York City on the Microsoft HoloLens. Experimental results indicate that remote rendering outperforms local rendering for the New York City model with significant improvement in average QoE by at least 21%. Additionally, we clarified the network traffic pattern in the proposed XR system developed under the OpenXR standard.

QAVA: QoE-Aware Adaptive Video Bitrate Aggregation for HTTP Live Streaming Based on Smart Edge Computing

Currently video streaming in heterogeneous network environments is affected by limited network bandwidth availability and consequent low and variable user Quality of Experience (QoE) levels. In particular, for the case of live video streaming, a very high number of end-clients request content at the same time, generating huge concurrent traffic, and putting pressure on the existing network infrastructure. An approach which helps address this issue is deployment of emerging edge computing technologies to smooth the live streaming traffic and improve QoE by adapting client bitrates and caching content at the edge server. In this context, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</b> oE-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> daptive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> ideo bitrate <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ggregation scheme for HTTP live streaming based on smart edge computing (QAVA). As an intelligent proxy server, a “smart edge” which deploys QAVA aggregates all the traffic requested by clients for the same live streaming service and adapts their bitrates based on network conditions, client states and video characteristics. The adaptation is performed based on a Deep Reinforcement Learning (DRL)-based algorithm, which is also proposed. The QAVA DRL algorithm is trained and modeled based on a real client experience dataset. The experimental evaluation results presented in this paper show how QAVA outperforms other state-of-the-art adaptive bitrate algorithms in terms of average QoE and QoE fairness.

Value-based reinforcement learning approaches for task offloading in Delay Constrained Vehicular Edge Computing

In the age of booming information technology, human-being has witnessed the need for new paradigms with both high computational capability and low latency. A potential solution is Vehicular Edge Computing (VEC). Previous work proposed a Fuzzy Deep Q-Network in Offloading scheme (FDQO) that combines Fuzzy rules and Deep Q-Network (DQN) to improve DQN’s early performance by using Fuzzy Controller (FC). However, we notice that frequent usage of FC can hinder the future growth performance of model. One way to overcome this issue is to remove Fuzzy Controller entirely. We introduced an algorithm called baseline DQN (b-DQN), represented by its two variants Static baseline DQN (Sb-DQN) and Dynamic baseline DQN (Db-DQN), to modify the exploration rate base on the average rewards of closest observations. Our findings confirm that these baseline DQN algorithms surpass traditional DQN models in terms of average Quality of Experience (QoE) in 100 time slots by about 6%, but still suffer from poor early performance (such as in the first 5 time slots). Here, we introduce baseline FDQO (b-FDQO). This algorithm has a strategy to modify the Fuzzy Logic usage instead of removing it entirely while still observing the rewards to modify the exploration rate. It brings a higher average QoE in the first 5 time slots compared to other non-fuzzy-logic algorithms by at least 55.12%, prevent the model from getting too bad result over all time slots, while having the late performance as good as that of b-DQN.

RAPT360: Reinforcement Learning-Based Rate Adaptation for 360-Degree Video Streaming With Adaptive Prediction and Tiling

Tile-based rate adaption can improve the quality of experience (QoE) for adaptive 360-degree video streaming under constrained network conditions, which, however, is a challenging problem due to the requirements of accurate prediction for users&#x2019; viewports and optimal bitrate allocation for tiles. In this paper, we propose a strategy that deploys reinforcement learning-based Rate Adaptation with adaptive Prediction and Tiling for 360-degree video streaming, named RAPT360, to address these challenges. Specifically, to improve the accuracy of the state-of-the-art viewport prediction approaches, we fit the time-varying Laplace distribution-based probability density function of the prediction error for different prediction lengths. On the basis of that, we develop a viewport identification method to determine the viewport area of a user depending on the buffer occupancy, where the obtained viewport can cover the real viewport with any given probability confidence level. We then propose a viewport-aware adaptive tiling scheme to improve the bandwidth efficiency, where three types of tile granularities are allocated according to the shape and position of the 2-D projection of that viewport. By establishing an adaptive streaming model and QoE metric specific to 360-degree videos, we finally formulate the rate adaptation problem for tile-based 360-degree video streaming as a non-linear discrete optimization problem that targets at maximizing the long-term user QoE under a bandwidth-constrained network. To efficiently solve this problem, we model the rate adaptation logic as a Markov decision process (MDP) and employ the deep reinforcement learning (DRL)-based algorithm to dynamically learn the optimal bitrate allocation of tiles. Extensive experimental results show that RAPT360 achieves a performance gain of at least 1.47 dB on average chunk QoE, including a video quality improvement of at least 1.33 dB, in comparison to the existing strategies for tile-based adaptive 360-degree video streaming.

QoE Driven VR 360° Video Massive MIMO Transmission

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.

QoE-driven Antenna Tuning in Cellular Networks With Cooperative Multi-agent Reinforcement Learning

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.

Research on handover strategy of heterogeneous networks based on user service quality

SummaryIn view of the handover problem of visible light communication (VLC) and WiFi, most of existing works focus on optimization of vertical handover algorithm of heterogeneous network. However, cooperative handover strategy of horizontal handover (HHO) and vertical handover (VHO) was rarely studied. In the situation, this paper proposes a cooperative horizontal VHO (CHVHO) strategy for heterogeneous network based on quality of experience (QoE). This strategy mainly coordinates the HHO and VHO of the network from the aspects of building servers and the simplification of indoor movement model, which improves the QoE allocated to users. Simulation results demonstrate that, compared with traditional immediate VHO (IVHO) and dwell VHO (DVHO), this strategy can be adaptive to user movement and get higher average QoE.