HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the user's play-out buffers.

In HTTP adaptive streaming (HAS) applications multiple video clients sharing the same wireless channel may experience different video qualities as result of both different video content complexity and different channel conditions. This causes unfairness in the end-user video quality. In this paper, we propose a quality-fair adaptive streaming (QFAS) solution to deliver fair video quality to HAS clients competing for the same resources in an LTE cell. In the QFAS framework the share of radio resource is optimized according to video content characteristics and channel condition. The proposed solution is compared with other state-of-the-art strategies and numerical results in terms of SSIM quality metric shows that it significantly improves the quality fairness among heterogeneous HAS users.

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.

This 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.

This 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, MAC-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.

This 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.

In 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.

Statistical multiplexing aims at transmitting several variable bit rate (VBR) encoded video streams over a bandlimited channel. Rate-distortion (RD) models for the encoded streams are often used to control the video encoders. As discrepancies frequently occur between the actual RD characteristics and their models, buffers are placed at the output of each coder to facilitate regulation. In this paper, a statistical multiplexer is proposed where video coders and buffers are controlled in a closed loop. First, a predictive joint rate controller accounting for maximum distortion, fairness, and smoothness constraints is considered. Second, all buffers are controlled simultaneously to limit deviations from a reference buffer occupancy to prevent buffer under and overflow. The main idea is to update the encoding rate for each encoding unit according to the average level of the buffers, to maximize the quality of each program. Simulation results show that the proposed scheme yields a smooth and fair video quality among programs thanks to the predictive control and allows an efficient use of the available bandwidth.

The control of the trade-off between encoding rate and quality of compressed video is a challenging task. This control requires efficient rate and distortion (R-D) models, able to describe accurately the behavior of the compressed video. R-D models should account for the dependencies induced by the choice of frame-varying quantization parameters (QP). This paper proposes a dependent R-D model involving two independent regime-switching autoregressive (IRSAR) models. Experimental results show that the proposed model is able to represent accurately the distortion dependency between frames. For the part of the rate due to the texture, the model fits experimental curves reasonably well. The model has to be completed to account also for the part of the rate due to motion vectors and signalization.

This paper addresses the problem of video on demand delivery over a time-varying wireless channel. Packet scheduling and buffer management are jointly considered for scalable video transmission to adapt to the changing channel conditions. A proxy-based filtering algorithm among scalable layers is considered to maximize the decoded video quality at the receiver side while keeping a minimum playback margin. This problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Experimental results illustrate the benefit of this approach compared to a shortterm policy in term of average PSNR improvement.