Quality Of Experience For Video Streaming Research Articles

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.

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.

Bayesian K-means clustering based quality of experience aware multimedia video streaming

<span>Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.</span>

Predicting the QOE of Video Streaming in Communication Networks

The Video streaming QoE (Quality of Experience) index consists of a series of qualitative factors that are difficult to measure. On the other hand, other Video streaming indices, such as the KPIs (Key Performance Indicators) are easily and physically measurable. 
 
 This paper introduces a method to predict QoE based on KPIs measures in (wired or wireless) communication networks. Besides the possible academic interest, the method may practically be of interest to the network operator. Indeed, to ensure compliance with the SLA (Service Level Agreement) he would like to predict how the QoE can change as a consequence of a new network management alternative. 
 
 To perform prediction, the problem is that the network operator first needs to know how the KPIs would change due to the alternative, and then find a way to derive (say mathematically) the QoE from the new KPIs. 
 
 The contribution of this paper is a simulation/mathematical approach to solving the problem. First,  a simulation method is introduced to know how the KPIs would change as a consequence of the new alternative, and then a valid KPI/QoE mathematical relationship is introduced to derive the new QoE from the new KPIs.
 
 The paper is organized as follows: in Section 1 an introduction is given to the definition of QoE in Video streaming. In Section 2  the status of the art from the literature on the KPI/QoE mathematical models is dealt with, and a valid model is identified that derives the Video streaming QoE from the network KPIs. In Section 3, a simulated network is introduced to know how the KPIs would change as a consequence of a new network management alternative.  Finally, Section 4 uses the identified mathematical relationship to predict the Video streaming QoE from the measured KPIs. 
 
 The considered application is an LTE (Long Term Evolution) network, but the approach can be extended to any communication network, wired or wireless from 3G onwards.

Modeling Video Streaming Quality of Experience using Taguchi and Fuzzy Logic Methods

The popularity of mobile video streaming has increased significantly in recent years, and is expected to account for two-thirds of global internet traffic in the near future. However, determining accurately end-users' satisfaction based on network parameters remains a challenge. Existing research often uses network parameters, such as packet loss, delay, and jitter, to estimate users' Quality of Experience (QoE). However, most models present QoE estimates in Mean Opinion Scores (MoS), which are not easily understood by the customers. In this study, we used the Taguchi approach to conduct QoE experiments over a wireless tested. We investigated the simultaneous effects of packet loss, corruption, delay, and jitter on video streaming QoE, as well as their interaction effects. Furthermore, we developed a Fuzzy logic model in MatlabR2016a to establish the relationship between input variables and video streaming QoE. The model presents the results in an easily understandable linguistic terms such as excellent, good, average, bad, and poor. Additionally, the proposed model achieves a correlation of 0.875 between the predicted and user scores, with a Root Mean Square Error of 0.344.

Analyzing the Influence of Smart-device Visual Features, Viewing Distance, and Content Factors on Video Streaming QoE

Quality of experience (QoE) over wireless networks has attracted attention from industry and academia due to an increase in video streaming applications. Several researchers have attempted to understand the factors affecting QoE and design appropriate quality control strategies. Normally, video streaming is initiated by a user who accesses video contents over wireless networks using a smart device held at various viewing distances. Each aforementioned factor has the potential to affect QoE of the viewed session. However, several studies explore the behavior of wireless networks on video streaming QoE. To understand the effects of other factors on QoE, this paper investigates the influence of the device's visual features, viewing distance, and content factors on video streaming. The study adopted an emulation technique to conduct multi-factor experiments designed using the Taguchi method. The 5-ways ANOVA analysis revealed that the effects of smart-device visual features, viewing distance, and content types are significant on video streaming QoE at p<0.05. Moreover, smart devices with a pixel density index of more than 200 ppi produce high QoE, with the viewing distance limited to 45 cm. Lastly, the video bitrate greater than 1024 kbps produced a good QoE regardless of the frame rates.

Multidimensional modelling of quality of experience for video streaming

The increasing use of video streaming services that followed the Covid-19 pandemic has more than ever driven a rising interest among various stakeholders in service provisioning chain to understand factors influencing quality of experience (QoE). Many research activities have so far addressed different influence factors in order to understand and improve QoE when using video streaming service. However, we have recognized the requirement to address QoE as multidimensional concept, and show the relationship between QoE, perceptual dimensions, and influence factors. In this paper, we provide the multidimensional modelling and analysis of QoE for video streaming. Result analysis has shown that QoE for video streaming can be modelled by using perception of quality of video and perception of ease of use of application as predictors. The analysis of influence of individual system (i.e., resolution, coding tree unit (CTU), and constant rate factor (CRF)), context (i.e., location, lighting, and video type), and human (i.e., gender, education level, and prior experience) factors and their interactions on QoE perceptual dimensions (i.e., perception of quality and perception of ease of use) show statistically significant impacts, which means that alternations of these factors can enhance perceptual dimensions and consequently QoE for video streaming which is a final goal.

A Survey on QoE-Oriented VR Video Streaming: Some Research Issues and Challenges

With the advent of the information age, VR video streaming services have emerged in large numbers in scenarios such as immersive entertainment, smart education, and the Internet of Vehicles. People are also demanding an increasing number of virtual-reality (VR) services, and service providers must ensure a good user experience. Therefore, the quality of the VR user’s experience is receiving increasing attention from academia and industry. The review in this paper focuses on a comprehensive summary of the current state of quality-of-experience (QoE) technologies applied to VR video streaming. First, we review the main influencing factors of QoE and VR video streaming. Second, the user QoE for VR evaluation is discussed. Third, the modeling of QoE for VR video streaming, the QoE-oriented VR optimization problem, and enabling techniques of machine learning for VR video streaming improvement are summarized. Lastly, we present current challenges and possible future research directions.

A Model for Mapping Combined Effects of Quality of Service Parameters and Device Features on Video Streaming Quality of Experience

Maintaining quality of streaming video is challenged by network faults resulting into freezes and rebufferings on the video. On top of the network effects, device features have impacts on the image of the video frames displayed during streaming. Despite the simultaneous impacts of video quality from network and device, previous studies considered individual impact of network parameters or devices as influencing factors to propose Quality of Experience (QoE) models. This study proposed QoE model by mapping combined effects from both network and device on video streamed QoE. An experiment to study the effects of video quality from combined effects of network and device over the wireless involved 35 subjects. Combination of packet loss, packet reordering, and delay were emulated using network emulator following Design of Experiment methodology. Through analysis of variance, the study found that packet loss had the highest impact, followed by device features, reordering, and delay on the video QoE. From the combined effects, two-way interactions and three-way interactions had significant effects on the video QoE. Through additive and linearity behavior of the input factors from network and device on video streaming QoE, a multi-factor model was derived. Keywords: Design of Experiment (DOE); Mean Opinion Score (MOS); Quality of Experience (QoE); Quality of Service (QoS); Video Quality Assessment

An Exploration-driven Reinforcement Learning Model for Video Streaming Scheduling in 5G-Powered Drone

In the process of wireless network video streaming, especially in more complex scenarios (such as video transmission of 5G-powered drones), analyzing the quality of experience (QoE) of the video streaming is a very crucial task. Thus attention should be paid to the dynamic interaction between QoE indicators including buffer starvation probability and traffic load. This paper proposes a video streaming scheduling model based on reinforcement learning. By learning the correlation between user behavior and traffic patterns, a series of resource allocation strategies that optimize QoE indicators are obtained. Since there is a certain degree of randomness in the network status at each moment in the transmission process, the model introduces exploration rewards to solve the noise problem of random environments. At the same time, this mechanism enables the model to fully explore the environment even when the reward is sparse, so as to obtain an effective scheduling strategy. Simulation experiments have proved that our model can improve the long-term QoE of video streaming in different network environments.

CoLEAP: Cooperative Learning-Based Edge Scheme With Caching and Prefetching for DASH Video Delivery

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

IJNM Special Issue—International Journal of Network Management: QoE‐centric analysis and management of communication networks

IJNM Special Issue—International Journal of Network Management: QoE‐centric analysis and management of communication networks

TCLiVi: Transmission Control in Live Video Streaming Based on Deep Reinforcement Learning

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.

QoE in video streaming over ad hoc networks: comparison and analysis of AODV and OLSR routing protocols

Video streaming services are easily among of the most solicited services on the internet. Indeed, they are single-handedly accountable for up to 85% of overall internet traffic. In this vein, new venues of marketing strategies are constantly witnessing the emergence of ever-higher quality standards emphasising on end-user appraisal. And so, in addition to the quality of service (QoS) which is built upon network-oriented metrics, it behooves us to pay greater heed to the quality of experience (QoE); whence ensuring decent overall performance re-emerges as a challenge to be reckoned with. In fact, the extant routing protocols tend to be network-oriented and are, as such, chiefly dependent upon objective quality parameters. This article purports to experiment on video transmission through an ad hoc network, based on the two emblematic routing protocols (i.e., AODV and OLSR), so as to pinpoint the one most in-step with the afore-mentioned subjective quality (QoE) trends.

A Proposed Measurement for Video Quality of Experience

Technological development in the last years leads to increase the access speed in the networks that allow a huge number of users watching videos online. The Quality of Experience (QoE) Knowledge of services that provide from the network is a very critical matter to have a strong design of multimedia streaming networks. This paper provides a video streaming QoE prediction metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that used to train the neural network and finally prediction the QoE value. Verify models prediction using 10-fold cross-validation that in a regular way split dataset (training set and test set) with multiple percentages. The proposed system verifies the best result.

QoE awareness in progressive caching and DASH-based D2D video streaming in cellular networks

In this paper, we present an architecture to improve video streaming quality of experience (QoE) in cellular networks with high user equipment (UE) density. In the proposed architecture, video segments are progressively cached, as requested, in selected UEs called storage members (SMs). Video segments are strategically cached to be available to requesting users in the cell. Furthermore, the base-station controls the device-to-device communication between the UEs to provide collaborative peer-to-peer transmission of video segments. Dynamic adaptive streaming over HTTP is also employed to adapt the quality of video segments to network conditions. We study the improvements achieved by the proposed architecture in terms of many video streaming QoE metrics. Thereafter, we improve the operation of the proposed architecture by introducing QoE awareness to both caching and distribution of video segments. We employ QoE awareness in three aspects of the proposed architecture; cellular resource allocation, caching of video segments, and SM-assignment optimization. We analyze the improvements achieved by the prospered QoE-awareness techniques in terms of video streaming QoE metrics.

QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Dynamic adaptive streaming over HTTP (DASH) is widely used for video streaming on mobile devices. Ensuring a good quality of experience (QoE) for mobile video streaming is essential, as it severely impacts both the network and content providers’ revenue. Thus, a good rate adaptation algorithm at the client end that provides high QoE is critically important. Recently, a segment size-aware rate adaptation (SARA) algorithm was proposed for DASH clients. However, its performance on mobile clients has not been investigated so far. The main contributions of this article are twofold: (1) We discuss SARA’s implementation for mobile clients to improve the QoE in mobile video streaming, one that accurately predicts the download time for the next segment and makes an informed bitrate selection, and (2) we developed a new parametric QoE model to compute a cumulative score that helps in fair comparison of different adaptation algorithms. Based on our subjective and objective evaluation, we observed that SARA for mobile clients outperforms others by 17% on average, in terms of the Mean Opinion Score, while achieving, on average, a 76% improvement in terms of the interruption ratio. The score obtained from our new parametric QoE model also demonstrates that the SARA algorithm for mobile clients gives a better QoE among all the algorithms.

From Theory to Practice

Modern video streaming uses adaptive bitrate (ABR) algorithms that run inside video players and continually adjust the quality (i.e., bitrate) of the video segments that are downloaded and rendered to the user. To maximize the quality-of-experience (QoE) of the user, ABR algorithms must stream at a high bitrate with low rebuffering and low bitrate oscillations. Further, a good ABR algorithm is responsive to user and network events and can be used in demanding scenarios such as low-latency live streaming. Recent research papers provide an abundance of ABR algorithms but fall short on many of the above real-world requirements. We develop Sabre, an open-source publicly available simulation tool that enables fast and accurate simulation of adaptive streaming environments. We empirically validated Sabre to show that it accurately simulates real-world environments. We used Sabre to design and evaluate BOLA-E and DYNAMIC, two novel ABR algorithms. We also developed a FAST SWITCHING algorithm that can replace segments that have already been downloaded with higher-bitrate (thus, higher-quality) segments. The new algorithms provide higher QoE to the user in terms of higher bitrate, fewer rebuffers, and lesser bitrate oscillations. In addition, these algorithms react faster to user events such as startup and seek, and they respond more quickly to network events such as improvements in throughput. Further, they perform very well for live streams that require low latency, a challenging scenario for ABR algorithms. Overall, our algorithms offer superior video QoE and responsiveness for real-life adaptive video streaming, in comparison to the state-of-the-art. Importantly, all three algorithms presented in this article are now part of the official DASH reference player dash.js and are being used by video providers in production environments. While our evaluation and implementation are focused on the DASH environment, our algorithms are equally applicable to other adaptive streaming formats such as Apple HLS.

Evaluation of Throughput Prediction for Adaptive Bitrate Control Using Trace-Based Emulation

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

Continuous Prediction for Quality of Experience in Wireless Video Streaming

Due to the rapid development of communication technologies, the requirement of mobile video streaming services is extremely increased in recent years. However, the bandwidth limitation of the wireless network often causes video impairments, such as compression artifacts and rebuffering event, when users are watching online videos. Hence, this problem often causes the reduction of quality of experience (QoE). Predicting the QoE can provide a reference to improve resource allocation strategies, accordingly providing users with a higher quality of video streaming services. In order to predict the impact of video impairment, continuous prediction for the QoE in wireless video streaming is proposed. The input of the predicted model consists of three vectors that characterize frame quality, the state of rebuffering events, and memory effect, respectively, while the output consists of continuous predicted the QoE. The predicted model uses a block-structured nonlinear Hammerstein-Wiener model. The experimental results confirm that our proposed model can effectively predict the continuous QoE for wireless video streaming.