Control of Multiple Remote Servers for Quality-Fair Delivery of Multimedia Contents

This paper proposes a control scheme for the quality-fair delivery of several encoded video streams to mobile users sharing a common wireless resource. Video quality fairness, as well as similar delivery delays are targeted among streams. The proposed controller is implemented within some aggregator located near the bottleneck of the network. The transmission rate among streams is adapted based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server (fully centralized solution), or directly evaluated by each server in a distributed way (partially distributed solution). Each encoding rate target is adjusted for each stream independently based on the corresponding buffer level or buffering delay in the aggregator. Communication delays between the servers and the aggregator are taken into account. The transmission and encoding rate control problems are studied with a control-theoretic perspective. The system is described with a multi-input multi-output model. Proportional Integral (PI) controllers are used to adjust the video quality and control the aggregator buffer levels. The system equilibrium and stability properties are studied. This provides guidelines for choosing the parameters of the PI controllers. Experimental results show the convergence of the proposed control system and demonstrate the improvement in video quality fairness compared to a classical transmission rate fair streaming solution and to a utility max-min fair approach

Online Learning for QoE-based Video Streaming to Mobile Receivers

International audienceThis paper proposes a cross-layer control mechanism to stream efficiently scalable videos to mobile receivers. Its goal is to maximize the quality of the received video while accounting for the variations of the characteristics of the transmitted content and of the channel. The control problem is cast in the framework of Markov Decision Processes. The optimal actions to apply to the system are learned using reinforcement learning. For that purpose, the quality of the decoded frames at receiver is inferred by an observation (i) of the quality of the various scalability layers and (ii) of the level of queues at the Application and Medium Access Control layers of the transmitter only. Delayed as well as absence of information on the channel state are considered. Experiments show that the performance of the proposed solution is only slightly degraded with delayed or missing channel state information. The performance degradation is larger when considering a basic bitstream extractor, which serves as reference

Predictive Encoder and Buffer Control for Statistical Multiplexing of Multimedia Contents

International audienceStatistical multiplexing of video contents aims at transmitting several variable bit rate (VBR) encoded video streams over a band-limited channel. Rate-distortion (RD) models for the encoded streams are often used to control the video encoders. Buffering at the output of encoders is one of the several techniques used to smooth out the fluctuating bit rate of compressed video due to variations in the activity of video contents. In this paper, a statistical multiplexer is proposed where a closed-loop control of both video encoders and buffers is performed jointly. First, a predictive joint video encoder controller accounting for minimum quality, fairness, and smoothness constraints is considered. Second, all buffers are controlled simultaneously to regulate the buffering delays. This delay is adjusted according to a reference delay constraint. The main idea is to update the encoding rate for each video unit according to the average level of the buffers, to maximize the quality of each program and effectively use the available channel rate. Simulation results show that the proposed scheme yields a smooth and fair video quality among programs thanks to the predictive control. A similar buffering delay for all programs and an efficient use of the available channel rate are ensured thanks to the buffer management and to the predictive closed-loop control

Statistical multiplexing of distributed video streams

National audienceWe consider a media aware network element (MANE) fed by several remote video servers. The role of the MANE is to bufferize the encoded video contents and to build a multiplex containing all video programs to be broadcasted or multicasted over a wireless link. We design a decentralized control technique able to satisfy some video quality fairness constraint among programs. Unlike most statistical multiplexing systems, our scheme is partly decentralized. The bandwidth allocation among programs is centralized and done within the MANE, but takes into account the quality fairness constraint. Each video server is controlled independently from the others, requiring no exchange between servers. The MANE feds back to each video server the level of its associated buffer to help the remote video servers to adapt their rate-distorsion trade-off so that the buffer reaches some reference level. Experimental results show that in the case of Gaussian sources, compressed and delivered to the MANE, an equilibrium is reached, and that the fairness constraint is satisfied

Control of Distributed Servers for Quality-Fair Delivery of Multiple Video Streams

International audienceThis paper proposes a quality-fair video delivery system able to transmit several encoded video streams to mobile users sharing some wireless resource. Video quality fairness, as well as similar delivery delay is targeted among streams. The proposed control system is implemented within some aggregator located near the bottleneck of the network. This is done by allocating the transmission rate among streams based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server, or directly evaluated by each server in a distributed way. Each encoding rate target is adjusted for each stream independently based on the corresponding buffering delay in the aggregator. The transmission and encoding rate control problems are addressed with a control-theoretic perspective. The system is described with a multi-input multi-output model and several Proportional Integral (PI) controllers are used to adjust the video quality as well as the buffering delay. The study of the system equilibrium and stability provides guidelines for choosing the parameters of the PI controllers. Experimental results show that better quality fairness is obtained compared to classical transmission rate fair streaming solutions while keeping similar buffering delays

Q-AIMD: A Congestion Aware Video Quality Control Mechanism

Following the constant increase of the multimedia traffic, it seems necessary to allow transport protocols to be aware of the video quality of the transmitted flows rather than the throughput. This paper proposes a novel transport mechanism adapted to video flows. Our proposal, called Q-AIMD for video quality AIMD (Additive Increase Multiplicative Decrease), enables fairness in video quality while transmitting multiple video flows. Targeting video quality fairness allows improving the overall video quality for all transmitted flows, especially when the transmitted videos provide various types of content with different spatial resolutions. In addition, Q-AIMD mitigates the occurrence of network congestion events, and dissolves the congestion whenever it occurs by decreasing the video quality and hence the bitrate. Using different video quality metrics, Q-AIMD is evaluated with different video contents and spatial resolutions. Simulation results show that Q-AIMD allows an improved overall video quality among the multiple transmitted video flows compared to a throughput-based congestion control by decreasing significantly the quality discrepancy between them

Class-Based MDP for improved multimedia transmission over LTE

International audienceThis paper proposes an improved cross-layer control mechanism to efficiently stream videos to mobile users over an LTE network. A proxy-based filtering algorithm among scalable layers is considered to decide the number of SVC layers to transmit for each frame according to the communication conditions and to the class to which the video belongs to. The problem is cast in the context of Markov Decision Processes which allow the design of foresighted policies maximizing some long-term accumulated reward. Optimal actions to apply to the system are obtained by reinforcement learning. The proposed solution is implemented in an LTE simulation platform. Experiments show the performance of the proposed class-based layer filtering algorithm for a single video transmission and its robustness to content changes

Cross-layer optimization of a multimedia streaming system via dynamic programming

International audienceThis paper addresses the problem of efficient video streaming to mobile users. A cross-layer optimization of various parameters of the coding and transmission chain (coding parameters, buffer management, AC-layer management) is performed to account for the time-varying nature of the characteristics of the transmitted contents and of the wireless channel. The problem is cast in the framework of Markov Decision Processes (MDP). This formalism provides efficient tools to compute a foresighted control policy maximizing some long term discounted sum of rewards linked to the video quality received by the user. Experimental results illustrate the benefits in terms of average PSNR of this approach compared to a short-term (myopic) policy. The robustness of the proposed control policy to variations of the transmitted contents is also illustrated

Adaptive Scalable Layer Filtering Process For video scheduling over wireless networks based on MAC buffer management

International audienceIn this paper, the problem of scalable video delivery over a time-varying wireless channel is considered. Packet scheduling and buffer management in both Application and Medium Access Control (MAC) layers are jointly considered. Various levels of knowledge of the state of the channel are considered. The control is performed via scalable layer filtering (some scalability layers may be dropped). In all cases, the problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Without channel state observation, the control has to rely on the observation of the level of the MAC buffer only. Experimental results show that even with a lack of knowledge of the channel state, the foresighted control policy provides only a moderate loss in received video quality

Quality-oriented control of video delivery over wireless channels

Le développement simultané de terminaux mobiles multimédia (smartphones, tablettes) et de réseaux d’accès offrant des débits élevés conduit à une explosion du trafic liés aux contenus multimédia. Cette croissance nécessite un partage efficace des ressources radio entre fournisseurs de contenus (dans le cas de la diffusion) ou entre récepteurs (dans le cas de services de vidéo à la demande). Cette thèse propose des outils de partage équitable des ressources en termes de qualité des contenus multimédia reçu et de délai de transmission dans les deux contextes précédents. La variété des compromis débit-distorsion des contenus multimédia est exploitée à cet effet. Dans un premier temps, une solution centralisée de contrôle conjoint du débit de codage et de transmission de plusieurs programmes transmis sur un même canal est considérée. L’objectif est de fournir des flux de qualités similaires avec des variations limitées, tout en assurant des délais de transmission comparables. Ce problème est résolu en synthétisant une commande prédictive à l’aide d’outils d’optimisation sous contrainte. Dans un second temps, seule l’allocation de bande est centralisée, le contrôle des caractéristiques de compression de chaque flux est réalisé de manière distribuée. Le contrôleur centralisé ne renvoie que le niveau de remplissage des tampons associés à chaque flux aux fournisseurs de contenus distants. Une stratégie de régulation des débits de codage est alors mise en place par ces fournisseurs, de manière à réguler le niveau en bits ou en image des tampons. La stabilité de ce système de régulation couplé est étudiée en détails. Enfin, l’optimisation inter-couches d’une chaine de transmission de contenus multimédia scalable est considérée. Ce problème est formulé dans le contexte de la programmation dynamique. Lorsque des modèles de complexité raisonnable sont considérés et avec des caractéristiques du système bien connues, des solutions optimales peuvent être obtenues. Des techniques d’apprentissage sont mises en œuvre, lorsque le système n’est que partiellement connu, par exemple, lorsque l’état du canal de transmission parvient avec du retard à l’organe de commande.Due to the emergence of new generation mobiles and media streaming services, data traffic on mobile networks is continuously exploding. Despite the emergence of standards such as LTE, resources still remain scarce and limited. Thus, efficiently sharing resources among broadcasters or between unicast receivers connected to the same base station is necessary. An efficient resources allocation, where a fair received video quality between users and an equal transmission delay are achieved, is targeted. To that end, the variety of the rate-distortion trade-off of multimedia content is exploited. First, a centralized joint encoding and transmission rate control of multiple programs sharing the same channel is considered. A satisfactory and a comparable video quality among the transmitted programs, with limited variations, as well as a comparable transmission delay are targeted. The problem is solved using constrained optimization tools. Second, only the bandwidth allocation control is centralized, the control of the encoding rate characteristics of each stream is carried in a distributed manner. By modeling the problem as a feedback control system, the centralized bandwidth allocation is required to feed back only the buffer level to its associated remote content provider. The equilibrium and stability issues are addressed for both bit and second buffer control. In the case of simple unicast connection, a cross-layer optimization of scalable video delivery over wireless channel is performed. The optimization problem is cast in the context of dynamic programming. When low complex model are considered and when the system characteristics are known, optimal solutions can be obtained. When the system is partially known, for example, when the state of the channel reaches the control process with delay, learning techniques are implemented