Quality Of Experience Fairness Research Articles

QoE‐driven multi‐UAV deployment scheme for emergency communication networks

AbstractWhen ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)‐mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line‐of‐sight and non‐line‐of‐sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs.

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.

Achieving QoE Fairness in Bitrate Allocation of 360° Video Streaming

In tile-based 360° video streaming, the users employ the tile rate allocation algorithm to select appropriate bitrate to maximize the quality of experience (QoE). The preferences and viewports, however, can vary significantly across the different users. Since the users independently choose their bitrate according to their own preferences and viewports, it is hard to ensure QoE fairness for users under the constraint of available bandwidth. In this paper, we propose a QoE-fairness aware bitrate allocation algorithm for multi-users (QBAM) to reduce difference of user QoE. According to the trajectory of the user viewpoint and user preferences for video quality, rebuffer time and quality switching, we leverage multi-agent reinforcement learning to train the bitrate allocation strategy. The experimental results show, compared with the current tile rate allocation algorithm, QBAM effectively improves the QoE fairness.

HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance

As the number of users and the types of videos viewed increase, seamless video streaming services are becoming more important. Adaptive streaming aims to achieve high Quality of Experience (QoE) in time-varying network conditions. However, the existing schemes lack considerations for quality adaptation to improve QoE under dynamic network environments and multi-client competition. In this paper, we propose an HTTP adaptive streaming scheme based on reinforcement learning with edge computing assistance. The proposed scheme uses reinforcement learning to generate robust adaptation policy for changes in the network conditions. Edge computing plays a role of managing multiple clients based on information about the network and client. The proposed scheme considers subjective quality, multiple videos, and learning variances to advance the adaptation policy. By balancing between individual QoE and QoE fairness, the proposed scheme enables multiple clients to utilize the bandwidth as much as required. Experimental results show that the proposed scheme has better performance for individual QoE and QoE fairness than the existing schemes.

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.

Learning-Based Joint QoE Optimization for Adaptive Video Streaming Based on Smart Edge

The latest increase in HTTP-based adaptive video streaming over the Internet enables a growing number of clients to compete for a shared bottleneck bandwidth. This competition may affect users&#x2019; Quality of Experience (QoE) negatively, especially in terms of fairness and stability. This paper presents <i>Flex-Steward</i>, a solution that performs multi-client joint QoE optimization for adaptive video streaming during bottleneck bandwidth sharing. Joint QoE optimization refers to improving QoE fairness among clients with various video devices and availing from differentiated services with different priorities. Flex-Steward deploys an adaptive bitrate delivery algorithm based on Neural Networks (NN) and reinforcement learning at the network edge. It relies on a trained NN model to make appropriate bitrate recommendations in terms of video chunks to be requested by clients sharing the same bottleneck bandwidth. Flex-Steward is assessed in comparison with alternative state-of-the-art algorithms under different network conditions using a real-life prototype. Results show how Flex-Steward reduces the unfairness in terms of joint QoE optimization with between 10.9&#x0025; and 41.7&#x0025;.

Reinforcement Learning-Based Adaptive Streaming Scheme with Edge Computing Assistance.

Dynamic Adaptive Streaming over HTTP (DASH) is a promising scheme for improving the Quality of Experience (QoE) of users in video streaming. However, the existing schemes do not perform coordination among clients and depend on fixed heuristics. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme improves the overall QoE of clients and QoE fairness among clients based on a state-of-the-art reinforcement learning algorithm. Edge computing assistance plays a role in providing client-side observations to the mobile edge, making agents utilize this information when generating a policy for multi-client adaptive streaming. We evaluated the proposed scheme through simulation-based experiments under various network conditions. The experimental results show that the proposed scheme achieves better performance than the existing schemes.

Reinforcement Learning-Based HTTP Adaptive Streaming Scheme in Edge Computing Environments

Dynamic Adaptive Streaming over HTTP (DASH) is a representative scheme to improve Quality of Experience (QoE) of video streaming users. However, the QoE fairness is degraded when multiple users stream videos over the same network. The reasons of this problem are a lack of coordination among clients and bitrate adaptation with the fixed heuristics. In this paper, we propose an HTTP adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme generates a policy based on reinforcement learning to optimize QoE fairness and utilizes edge computing to assist policy generation. We evaluated the proposed scheme through simulations under various network conditions. The experimental results show that the proposed scheme improves QoE fairness of multiple clients compared with the existing scheme.

Slicing Wi‐Fi links based on QoE video streaming fairness

SummaryDynamic Adaptive Streaming over HyperText Transfer Protocol (DASH) video streaming is one of the dominant sources of traffic on the Internet, and this traffic is often delivered to users via Wi‐Fi access points. This makes the quality of experience (QoE) of the service susceptible to degradation when the Wi‐Fi link is underperforming, and it can also inflict QoE unfairness, creating situations where users connected to the same Wi‐Fi network feel different levels of QoE. This article presents a system that improves the QoE fairness of DASH video transmission network slicing in the context of Wi‐Fi networks. The proposed system slices the wireless resources unevenly among flows, taking into account the characteristics of the video and the QoE demands for each user. Experimental results show that fairness improves by 56% when compared to an unmanaged network, while the mean QoE is reduced by only 5%.

High system capacity pre-scheduler for multi-cell wireless networks

With the constant increase of throughput demands, maximizing the wireless network capacity has always been a crucial issue. Network densification becomes one of the adopted strategies to guarantee a high Quality of Experience (QoE). These new access points are included in the traditional 5G coverage scheme and offload the network in dense areas where the demand is high. A mobile may be located in the coverage area of several base stations which implies the need to extend traditional scheduling to a multicell context. This leads to the emergence of new issues concerning resource allocation strategies. When one user is suitable to receive resources from different cells, a well designed users' distribution is essential to limit unbalanced and overloaded cells. The main contribution of this paper is to propose a pre-scheduler called Multi-Cell Pre-Scheduler (MCPS) that adequately allocates user traffic between different access points, intervening before the scheduling application. Compatible with any type of simple cell existing schedulers, MCPS adapts schedulers to multi-cell context and improves their performance by delaying the system congestion and making the system more robust to unexpected traffic peaks. Performance evaluations show that the use of MCPS solution allows to increase the throughput capacity and energy efficiency while improving QoE fairness between users.

Multi-User Adaptive Video Delivery Over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach

In this paper, we investigate the adaptive video delivery for multiple users over time-varying and mutually interfering multi-cell wireless networks. The key research challenge is to jointly design the physical-layer resource allocation scheme and application-layer rate adaptation logic, such that the users' long-term fair quality of experience (QoE) can be maximized. Due to the timescale mismatch between these two layers and the asynchrony of user requests, however, it is difficult to directly model the cross-layer stochastic control problem by using a reinforcement learning framework. To address this difficulty, we propose a novel two-level decision framework where an optimization-based beamforming scheme (performed at the base stations) and a deep reinforcement learning (DRL)-based rate adaptation scheme (performed at the user terminals) are, respectively, developed, such that a highly complex long-term multi-user QoE fairness problem is decomposed into some relatively simple problems and solved effectively. Our strategy represents a significant departure from the existing schemes with consideration of either a short-term multi-user QoE maximization or a long-term single-user point-to-point QoE maximization. Extensive simulations demonstrate that the proposed cross-layer design is effective and promising.

A Fair Share for All: TCP-Inspired Adaptation Logic for QoE Fairness Among Heterogeneous HTTP Adaptive Video Streaming Clients

This paper presents a novel adaptation logic for HTTP adaptive streaming (HAS), which achieves not only a high quality of experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known transmission control protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this paper, the design of the TCP-inspired adaptation logic (TCPAL) is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS adaptation logics. TCPAL is evaluated both in scenarios with stable and fluctuating streaming capacity, and the impact of its parameters is explored. The results suggest that TCPAL performs on par with other HAS adaptation logics in terms of QoE and QoE fairness for low link capacities, but significantly improves the QoE fairness for increased link capacity. Moreover, the fairness achieved by TCPAL does not degrade in situations with fluctuating streaming capacity.

ORL-SDN

In designing an HTTP adaptive streaming (HAS) system, the bitrate adaptation scheme in the player is a key component to ensure a good quality of experience (QoE) for viewers. We propose a new online reinforcement learning optimization framework, called ORL-SDN, targeting HAS players running in a software-defined networking (SDN) environment. We leverage SDN to facilitate the orchestration of the adaptation schemes for a set of HAS players. To reach a good level of QoE fairness in a large population of players, we cluster them based on a perceptual quality index. We formulate the adaptation process as a Partially Observable Markov Decision Process and solve the per-cluster optimization problem using an online Q-learning technique that leverages model predictive control and parallelism via aggregation to avoid a per-cluster suboptimal selection and to accelerate the convergence to an optimum. This framework achieves maximum long-term revenue by selecting the optimal representation for each cluster under time-varying network conditions. The results show that ORL-SDN delivers substantial improvements in viewer QoE, presentation quality stability, fairness, and bandwidth utilization over well-known adaptation schemes.

Quality of Experience Driven Rate Adaptation for Adaptive HTTP Streaming

A quality of experience (QoE) driven rate adaptation approach is proposed in this paper for adaptive HTTP streaming which jointly considers both bandwidth savings and video quality adaptation into account for the rate adjustment, beneficial to both service providers and subscribers. At each decision epoch, the adaptation algorithm accumulates for the time varying QoE of a viewer by accounting for all the impairments, namely, initial delay, quality transitions, and playback interruptions, which commonly occur during the playback of a video in adaptive streaming. Based on the possible bandwidth savings and resulting QoE variations, the algorithm decides on to adapt the bitrate dynamically and accordingly maximizes viewers’ QoE. Since the proposed adaptation approach constructs an optimal path across the segments for adaptation, it also achieves QoE fairness among multiple clients in shared bandwidth environments. The experimental evaluation carried over real-world wireless network environments demonstrate that the proposed adaptation approach can maximize viewers’ QoE in adaptive streaming, especially, under highly variable throughput conditions.

QoE-Aware OTT-ISP Collaboration in Service Management

It is a matter of fact that quality of experience (QoE) has become one of the key factors determining whether a new multimedia service will be successfully accepted by the final users. Accordingly, several QoE models have been developed with the aim of capturing the perception of the user by considering as many influencing factors as possible. However, when it comes to adopting these models in the management of the services and networks, it frequently happens that no single provider has access to all of the tools to either measure all influencing factors parameters or control over the delivered quality. In particular, it often happens to the over-the-top (OTT) and Internet service providers (ISPs), which act with complementary roles in the service delivery over the Internet. On the basis of this consideration, in this article we first highlight the importance of a possible OTT-ISP collaboration for a joint service management in terms of technical and economic aspects. Then we propose a general reference architecture for a possible collaboration and information exchange among them. Finally, we define three different approaches, namely joint venture, customer lifetime value based, and QoE fairness based. The first aims to maximize the revenue by providing better QoE to customers paying more. The second aims to maximize the profit by providing better QoE to the most profitable customers (MPCs). The third aims to maximize QoE fairness among all customers. Finally, we conduct simulations to compare the three approaches in terms of QoE provided to the users, profit generated for the providers, and QoE fairness.

A new QoE fairness index for QoE management

The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

Quantitative comparison of application–network interaction: a case study of adaptive video streaming

Managing quality of experience (QoE) is now widely accepted as a critical objective for multimedia applications and the supporting communication systems. In general, QoE management encompasses: (1) monitoring of the key influence factors and QoE indicators, and (2) deciding on the appropriate control actions as specified by the management goal. Many multimedia applications, e.g., video streaming and audio conferencing, are able to adjust their operational parameters so as to react to variations in the network performance. However, such an adaptation feature is mostly based on a local client view of the network conditions, which may lead to an unfair allocation of network resources among heterogeneous clients and, thus, an unfair QoE distribution. In order to tackle this issue, there is the call for a cooperation between the applications and the underlying network, which includes application–network interaction (App-Net) in terms of: (1) exchanging information on the monitored QoE indicators, and (2) coordinating the QoE control actions. Various App-Net mechanisms focusing on specific use cases and applications have been proposed to date. This paper gives an overview of App-Net mechanisms and proposes a generic App-Net model that provides the means to realize a coordinated QoE-centric management. Based on the App-Net model, we develop an evaluation methodology to compare three App-Net mechanisms for managing QoE of HTTP adaptive streaming (HAS) against a baseline HAS service. The aim of this quantitative comparison is to explore the trade-offs between QoE gains and the complexity of App-Net implementation, with respect to the number of monitoring and control messages, achieved video quality, and QoE fairness among heterogeneous clients. Our ultimate goal is to set up reproducible experiments that facilitate a holistic evaluation of different App-Net mechanisms.

Q-FDBA: improving QoE fairness for video streaming

Multiplayer video streaming scenario can be seen everywhere today as the video traffic is becoming the “killer” traffic over the Internet. The Quality of Experience fairness is critical for not only the users but also the content providers and ISP. Consequently, a QoE fairness adaptive method of multiplayer video streaming is of great importance. Previous studies focus on client-side solutions without network global view or network-assisted solution with extra reaction to client. In this paper, a pure network-based architecture using SDN is designed for monitoring network global performance information. With the flexible programming and network mastery capacity of SDN, we propose an online Q-learning-based dynamic bandwidth allocation algorithm Q-FDBA with the goal of QoE fairness. The results show the Q-FDBA could adaptively react to high frequency of bottleneck bandwidth switches and achieve better QoE fairness within a certain time dimension.

Definition of QoE Fairness in Shared Systems

User-centric service and application management focuses on the quality of experience (QoE) as perceived by the end user. Thereby, the goal is to maximize QoE while ensuring fairness among users, e.g., for resource allocation and scheduling in shared systems. Although the literature suggests to consider consequently QoE fairness, there is currently no accepted definition of QoE fairness. The contribution of this letter is the definition of a generic QoE fairness index $F$ , which has desirable key properties as well as the rationale behind it. By using examples and a measurement study involving multiple users downloading web content over a bottleneck link, we differentiate the proposed index from QoS fairness and the widely used Jain’s fairness index. Based on results, we argue that neither QoS fairness nor Jain’s fairness index meet all of the desirable QoE-relevant Properties, which are met by $F$ . Consequently, the proposed index $F$ may be used to compare QoE fairness across systems and applications, thus serving as a benchmark for QoE management mechanisms and system optimization.

Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies.