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Different from traditional HTTP adaptive streaming (HAS) in which only one client is considered, HAS over multi-client wireless networks faces new challenges. The Quality of Experience (QoE) of users becomes unstable due to users' competition for shared bandwidth. It is thus important to accurately estimate the perceived experience of users and the...
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... we evaluate the QoE fairness using the normalized difference of a certain metric between the two users, i.e., the difference of two values divided by the larger value. The normalized difference of APQ and PS is shown in Table 4. We observe that the proposed algorithm shows the least QoE difference between two users and thus guarantee the QoE fairness. ...
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Dynamic adaptive streaming over HTTP (DASH) clients compete with each other over one or more bottleneck links in a network, which results in fluctuations in TCP throughput and QoE, QoE unfairness among clients, and underutilization of the network capacity. We propose centralized and distributed architectures for collaboration between network servic...
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... Through a heuristic-based algorithm, this scheme determines the video bitrates of multiple clients that minimize unnecessary traffic and QoE degradation. The other approaches to improve the performance of multi-client adaptive streaming have focused on moving adaptation intelligence to servers or in-network elements [19][20][21][22]. The key idea of these approaches is to supervise clients in a centralized manner by aggregating the information of clients. ...
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.
... This results in quality fluctuation and decreases the clients' QoE. QoE model-based resource allocation algorithm [26][27][28][29]: The QoE level is predicted mostly in terms of two aspects including objective video quality and the subjective perceptual quality. Objective video quality indicators include MSE, PSNR, SSIM etc. ...
With the rapid development of dynamic adaptive streaming over HTTP (DASH) services, how to satisfy the requirements of DASH clients has attracted more and more attention. This study was carried out to focus on the quality of experience (QoE) modelling and the model‐based cross‐layer design which consists of segment adaptation and resource allocation, improving the playback experience of the clients. First, the key factors are investigated, which can affect the clients’ subjective satisfaction. A characteristics and playback information of the segments‐based QoE (CPIQ) model is established by employing the curve‐fitting method based on a large amount of subjective experimental results of these factors. Then with the CPIQ model as the objective function, a cross‐layer design CPIQ model‐based joint algorithm of segment request and resource allocation (CJRRA) model is formulated to maximise the total QoE of all the clients subject to the network resource and segment representation constraints. Segment adaptation and resource allocation strategy can be determined jointly by the authors developed low complexity solution. Finally, the simulation results show that the overall performance of CPIQ model and CJRRA significantly outperforms other compared models or algorithms in terms of accuracy, linearity and stability.
... Other studies have tested adaptation logic without user buffer level consideration [36,43]; e.g., the adaptation logic relies on bandwidth estimation or the idle time between two consecutive GET requests for media segments to estimate the network conditions. Adaptation logic's that estimate available bandwidth from several parameters such as the measured bandwidth from the last segment download [41,42,54] have also been considered. Other works [19,29,38,48,50] focus on adaptation logic that estimates the bandwidth of the last n segments' bandwidth (multi segment based bandwidth estimation). ...
The increasing popularity of online video content and adaptive video streaming services, especially those based on HTTP Adaptive Streaming (HAS) highlights the need for streaming optimization solutions. From a server perspective, the main drawback of HAS is that the user selects the quality of the next video segment without taking the server constraints into account. These constraints include the number of users simultaneously being served and the server’s congestion. Here, we present the Fair Server Adaptation (FSA) algorithm, which is designed to maximize user Quality of Experience (QoE) by tackling the server’s bottleneck problem. The algorithm provides the quality representation that is closest to the user’s request, subject to the server’s constraints. Simulation results show that compared to standard Dynamic Adaptive Streaming over HTTP (DASH) server, FSA increased the number of served users and decreased both the number of rebuffering events and the average rebuffering event duration. Furthermore, the average number of unserved users decreased to almost zero and Jain’s fairness index rose. It is clear that these changes increase users’ QoE.
... The objective of the proposed algorithm is to maximize users' perceived quality and to achieve QoE-fairness among them. Unlike previous works for achieving QoE-fairness [29] [7] [4], our algorithm takes quality variations into consideration. Furthermore, current QoE-fairness approaches are not directly applicable to freeviewpoint videos since they only optimize the resource allocation based on the quality of a single view. ...
... However, the utility function used by the algorithm is not content-aware since it is not based on a video quality metric. In [29], a QoE continuum model which considers both cumulative playback quality and playback smoothness using an exponential weighted moving average is presented. Based on this model, a quality adaptation algorithm is proposed that can guarantee both QoE and fairness between multiple clients in a cellular network by exploiting the nature of human perception and video source. ...
Free-viewpoint video (FVV) applications enable viewers to interactively change their viewing point and watch a scene from different angles. Each FVV is composed of multiple streams representing the scene and its geometry from different vantage points. In addition, virtual views can be synthesized from captured views to provide a smoother and more immersive experience to users. Delivering FVV streaming services over cellular networks, while achieving quality-of-experience (QoE) fairness and minimizing fluctuations in perceived quality, is very challenging due to the large bandwidth requirements, the complex relationship between the bitrates of the transmitted streams and the quality of rendered virtual views, and the time-varying channel conditions. In this paper, we formulate FVV adaptive streaming as a multi-objective QoE-fairness problem and propose a heuristic algorithm to solve it efficiently. Our experiments show that the proposed algorithm achieves high QoE-fairness and provide users with high and stable qualities. It reduces quality variations by up to 32% on average while saving up to 18% of cellular bandwidth, compared to state-of-the-art approaches.
... • Introduce the QoE (Quality-of-Experience) metric (e.g., the QoE continuum [44]) into the optimization framework of ROCHET to improve the user experience. • Study the problem of concurrent HFR video transmission with Multipath TCP (MPTCP) [45], [51] to multihomed mobile devices in heterogeneous wireless networks [35]. ...
High-frame-rate (HFR) video technology is becoming widely implemented in popular multimedia applications (e.g., Youtube and cloud gaming) to provide a smooth viewing experience, while transmission control protocol (TCP) is pervasively adopted as the transport-layer solution for video communications to achieve firewall traversal and network friendliness. However, it is severely challenging to effectively deliver real-time HFR video over TCP: 1) HFR video streaming features high transmission rate, enhanced frame density, and stringent delay constraint and 2) the packet retransmission and congestion control mechanisms in TCP may cause frequent throughput fluctuations and deadline violations. Motivated by addressing these critical issues, this research presents an application-layer forward error correction (FEC) framework dubbed Raptor coded HFR video over TCP (ROCHET). First, we develop a mathematical model to analyze the frame-level distortion of systematic Raptor code-based HFR video communication over TCP in wireless networks. Second, we propose a joint approximate distortion estimation and Raptor coding adaption solution to minimize the sum of total distortion. The proposed ROCHET is able to effectively leverage unequal error protection and TCP state analysis to enhance streaming video quality. We conduct the performance evaluation through extensive emulations in Exata involving real-time HFR video encoded with H.264 codec. Compared with the existing FEC coding schemes, ROCHET achieves appreciable improvements in terms of video peak signal-to-noise ratio, goodput, and frame success rate. Thus, ROCHET is recommended for TCP-based HFR video transmission over wireless networks.
... A QoE continuum driven HAS over multi-client wireless networks was preliminarily developed without the consideration of edge cloud in [20]. In this new research, taking the advantage of edge cloud's recent advances, we have substantially re-designed the framework: (1) an hybrid edge cloud and client side adaptation architecture, (2) an edge cloud assisted optimized rate adaptation strategy, (3) an improved channel estimation, and (4) an enhanced system evaluations. ...
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 application-layer and radio access networks (RAN) 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 as well as 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 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 environment. Furthermore, Prius adaptation algorithm achieves a near-optimal performance that outperforms exiting strategies.
... Nevertheless, many researches have used objective metrics related to video quality. For example, [157,78,154] use the average video encoding rate as objective metric. The logic behind this is that users generally prefer to watch high video quality during time. ...
... QoE UnfairnessZ. Yan et al.[154] addressed the issue of QoE unfairness between competing HAS players.This unfairness is a very valuable point. In fact we could have a mean measurement of QoE among players that it is satisfying, while one of more players have a poor measurement. ...
Le streaming vidéo adaptatif sur HTTP, couramment désigné par HTTP Adaptive Streaming(HAS), est une technique de streaming vidéo largement déployée sur le réseau Internet pour lesservices de vidéo en direct (Live) et la vidéo à la demande (VoD). Cette technique utilise le protocoleTCP comme protocole de transport. Elle consiste à segmenter la vidéo originale, stockéesur un serveur HTTP (serveur HAS), en petits segments (généralement de même durée de lecture)désignés par "chunks". Chaque segment de vidéo est transcodé à plusieurs niveaux de qualité,chaque niveau de qualité étant disponible sur un chunk indépendant. Le player, du côté duclient HAS, demande périodiquement un nouveau chunk une fois par durée de lecture du chunk.Dans les cas communs, le player sélectionne le niveau de qualité en se basant sur l’estimationde la bande passante du/des chunk(s) précédent(s). Étant donné que chaque clients HAS est situéau sein d’un réseau d’accès, notre étude se concentre sur un cas particulier assez fréquent dansl’usage quotidien : lorsque plusieurs clients partagent le même lien présentant un goulet d’étrangement(bottleneck) et se trouvants en état de compétition sur la bande passante. Dans ce cas, onsignale fréquemment une dégradation de la qualité d’expérience (QoE) des utilisateurs de HASet de la qualité de service (QoS) du réseau d’accès. Ainsi, l’objective de cette présente thèse estd’optimiser le protocole TCP pour résoudre ces dégradations de QoE et QoS.Notre première contribution consiste à proposer une méthode de bridage du débit HAS au niveaude la passerelle. Cette méthode est désignée par "ReceiveWindow Tuning Method" (RWTM)et elle consiste dans l’utilisation du principe de contrôle de flux de TCP et l’estimation passive dutemps d’aller retour au niveau de la passerelle. Nous avons comparé les performances de cetteméthode avec une autre méthode récente implémentée à la passerelle et utilisant une disciplineparticulière de gestion de la file d’attente, qui est désignée par "Hierarchical Token Bucket shapingMethod" (HTBM). Les résultats d’évaluations ont révélé que RWTM a non seulement unemeilleure QoE, mais aussi une meilleure QoS de réseau d’accès que pour l’utilisation de HTBM;plus précisément une réduction du délai de mise en file d’attente et une forte réduction du taux depaquets rejetés au niveau du goulot d’étrangement.Notre deuxième contribution consiste à mener une étude comparative combinant huit combinaisonsrésultant de la combinaison de deux méthodes de bridages, RWTM et HTBM, avec quatresvariantes TCP largement déployées, NewReno, Vegas, Illinois et Cubic. Les résultats de l’évaluationmontrent une discordance importante entre les performances des différentes combinaisons.De plus, la combinaison qui améliore les performances dans la majorité des scénarios étudiés estcelle de RWTM avec Illinois. En outre, nous avons révélé qu’une mise à jour efficace de la valeurdu paramètre "Slow Start Threshold", ssthresh, peut accélérer la vitesse de convergence du clientvidéo vers la qualité de vidéo optimale.Notre troisième contribution consiste à proposer une nouvelle variante de TCP adaptée auxflux HAS, qu’on désigne par TcpHas ; c’est un algorithme de contrôle de congestion de TCP quiprend en considération les spécifications de HAS. TcpHas estime le niveau de la qualité optimaledu flux HAS en se basant sur l’estimation de la bande passante de bout en bout. Ensuite, TcpHasapplique, d’une façon permanente, un bridage au trafic HAS en se basant sur le débit d’encodagedu niveau de qualité estimé. En plus, TcpHas met à jour ssthresh pour accélérer la vitesse deconvergence. Une étude comparative a été réalisée avec une variante de TCP, connue sous le nomWestwood+, qui utilise le mécanisme de la diminution adaptative. Les résultats de l’évaluation ontindiqué que TcpHas est largement plus performant queWestwood+ ; il offre une meilleure stabilitéautour de la qualité optimale, il réduit considérablement le taux de paquets rejetés au niveau dugoulet d’étrangement, et diminue le délai de la file d’attente.
... Essaili et al. [11] estimated the overall MOS using the selected bit-rate and content of the video. Yan et al. [12] estimation is computed based on the video smoothing streaming experience and a quantization parameter. Nevertheless, considering only these parameters is not sufficient since video resolution and video complexity are not taken into consideration. ...
Video streaming constitutes the vast majority of Internet traffic and the DASH protocol has become the de-facto standard in the industry of multimedia delivery. The multicast method for information distribution has the potential to dramatically reduce multimedia streaming traffic; however, to date, there is no effective Adaptive Logic (AL) designed to support multicast constraints at the client side. In this paper we present an adaptive logic that is designed specifically for multicast scenarios. A comparison of our Multicast Adaptive Logic (MAL) with leading ALs under multicast conditions demonstrates that MAL provides the best performance under multicast conditions and good performance under unicast conditions.
... The possibility of locating the adaptation in network has been shown in this paper. In [12], the adaptation problem is formulated as an optimization problem, which jointly determine the requests of all the UEs together. However, the playout continuity cannot be guaranteed by the model, which is an important QoE factor. ...
... The advantages of the model lie in two aspects. Firstly, compared with the model in [12], the adaptation decisions will be made more reasonably by formulating the constraint of the continuity. For instance, if the buffer level is low, T k is small and the UE would request for the low quality version to satisfy the constraint and keep the continuous playout. ...
... The compared algorithms involve some client-side schemes that solve the problem in the perspective of a single client(such as Throughput Based scheme (TB) [10] and Buffer Based scheme (BB) [9]), the existing scheme for multi-clients such as Continuum Driven adaptive scheme (CD) [12] and our proposed Playout-Buffer Aware adaptive Scheme over Multi-Client environment (PBMC). ...
Nowadays, HTTP adaptive streaming (HAS) has become a
cost-effective solution in delivering the video content. Different
from the one-client HAS, the HAS over multi-client
faces many challenges. Due to the lack of the knowledge of
the networks, one-client HAS will leads to the low efficiency
in utilizing the limited bandwidth over the multi-client
circumstance. In this paper, a playout buffer aware adaptation
scheme over multi-client (PBMC) LTE networks is proposed
to improve the performance of HAS. Firstly, a QoE
metric is given that considers both the static quality and
the quality smoothness. Secondly, to maximize the QoE, an
integer programming problem is formulated. By considering
the playout buffer level, the resource requirement, which
can guarantee the playout continuity, is formulated as a constraint
of the problem. Then, the feasibility of the problem
is analyzed. If the problem is infeasible, a method is given
to exclude the users as less as possible to obtain the feasibility.
When the problem is feasible, a graph model can
be constructed based on the resource requirement and the
playout buffer level. With the model, the solution can be
converted to finding a feasible path with the highest QoE in
the graph, which can be solved by employing the dynamic
programming. Finally, the simulation results can verify the
performance of PBMC in both improving the static quality
and guaranteeing the playout continuity.
... Proxy technologies have also been used to enhance QoE. [29] uses information about wireless channel quality at the base station to provide QoE and fairness among clients. [30] proposes WiDASH, a proxy for adaptive http streaming over wireless networks that implements a quadratic linear optimization problem to decide on the rate to use for each user/segment while giving higher priority to lower video rates. ...
The introduction of Dynamic Adaptive Streaming over HTTP (DASH) helped reduce
the consumption of resource in video delivery, but its client-based rate
adaptation is unable to optimally use the available end-to-end network
bandwidth. We consider the problem of optimizing the delivery of video content
to mobile clients while meeting the constraints imposed by the available
network resources. Observing the bandwidth available in the network's two main
components, core network, transferring the video from the servers to edge nodes
close to the client, and the edge network, which is in charge of transferring
the content to the user, via wireless links, we aim to find an optimal solution
by exploiting the predictability of future user requests of sequential video
segments, as well as the knowledge of available infrastructural resources at
the core and edge wireless networks in a given future time window. Instead of
regarding the bottleneck of the end-to-end connection as our throughput, we
distribute the traffic load over time and use intermediate nodes between the
server and the client for buffering video content to achieve higher throughput,
and ultimately significantly improve the Quality of Experience for the end user
in comparison with current solutions.
