Differentiated Services (DiffServ) is one of the leading architectures for providing quality of service in the Internet. We propose a scheme for real-time video transmission over a DiffServ network that jointly considers video source coding, packet classification, and error concealment within a framework of cost-distortion optimization. The selections of encoding parameters and packet classification are both used to manage end-to-end delay variations and packet losses within the network. We present two dual formulations of the proposed scheme: the minimum distortion problem, in which the objective is to minimize the end-to-end distortion subject to cost and delay constraints, and the minimum cost problem, which minimizes the total cost subject to end-to-end distortion and delay constraints. A solution to these problems using Lagrangian relaxation and dynamic programming is given. Simulation results demonstrate the advantage of jointly adapting the source coding and packet classification in DiffServ networks.

The problem of encoding and transmitting a video sequence over a wireless channel is considered. Our objective is to minimize the end-to-end distortion while using a limited amount of transmission energy and delay. In our approach, we jointly adapt the source-coding parameters and transmission power per packet. We introduce the concept of "variance-aware distortion estimation" (VADE), and present a framework for controlling both the expected value and the variance of the end-to-end distortion. This framework is based on knowledge of how the video is compressed, the probability of packet loss, and the concealment strategy. To the best of our knowledge, this paper is the first to address the trade-off between the mean and variance of the end-to-end distortion. Experimental results demonstrate the potential of the proposed approach.

This paper presents a novel framework for streaming video over a Differentiated Services (DiffServ) network that jointly considers video source coding, packet classification and error concealment within the scope of cost-distortion optimization. Our formulation incorporates the random network delay for each packet into the calculation of the probability of packet loss and manages the end-to-end packet delay by selecting the encoding parameters and packet priority. We formulate two approaches to evaluate the performance of the proposed framework: a minimum distortion approach and a minimum cost approach, in which the encoding mode and priority class for each packet are optimally selected so as to minimize the total distortion subject to cost constraints, or to minimize the total cost subject to end-to-end distortion constraints. Simulation results demonstrate the advantage of jointly adapting the source coding and packet classification.

Rapid growth in wireless networks is fueling demand for video services from mobile users. While the problem of transmitting video over unreliable channels has received some attention, the wireless network environment poses challenges such as transmission power management that have received little attention previously in connection with video. Transmission power management affects battery life in mobile devices, interference to other users, and network capacity. We consider energy efficient transmission of a video sequence under delay and quality constraints. The selection of source coding parameters is considered jointly with transmitter power and rate adaptation, and packet transmission scheduling. The goal is to transmit a video frame using the minimal required transmission energy under delay and quality constraints. Experimental results are presented that illustrate the advantages of the proposed approach.

We study some of the design tradeoffs of video streaming systems in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the quality of the received video sequence. We also consider the cost to the network user for streaming the video sequence. We have formulated this utility maximization problem as a joint constrained optimization problem where we maximize the difference between the utility and the network cost, subject to the constraint that the decoder buffer does not underflow. In this manner, we can find the optimal tradeoff between video quality and network cost. We present a deterministic dynamic programming approach for both the constant bit rate and renegotiated constant bit rate service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.

In the future, digital set-top boxes may serve as the primary access point for wireless home networks, enabling mobile users to use videoconferencing as well as streaming applications on hand-held devices. In this scenario, an important issue that must be addressed is the limited energy supply of a mobile device. This is of course a relevant issue for any wireless device. We focus on methods for efficiently utilizing transmission energy in wireless video communications. We present a general framework for the problem of minimizing the transmission energy required to provide an acceptable level of video quality. We discuss two special cases in which communication resources are adjusted simultaneously with the source coding parameters in order to provide (i) packet loss adaptation and (ii) transmission rate adaptation.

We consider the problem of compressing a video sequence for transmission over a wireless channel. In our approach we jointly consider error resilience and concealment techniques, at the source coding level, and transmission power management at the physical layer. We formulate a minimum-maximum distortion problem, where our goal is to either (i) minimize the total transmission energy for a given maximum expected distortion, or (ii) minimize the maximum expected distortion at the receiver for a given maximum transmission energy. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately.

We consider a situation where a video sequence is to be compressed and transmitted over a wireless channel. Our goal is to limit the amount of distortion in the received video sequence, while minimizing transmission energy. To accomplish this goal, we consider error resilience and concealment techniques at the source coding level, and transmission power management at the physical layer. We jointly consider these approaches in a novel framework. In this setting, we formulate and solve an optimization problem that corresponds to minimizing the energy required to transmit video under distortion and delay constraints. Experimental results show that simultaneously adjusting the source coding and transmission power is more energy efficient than considering these factors separately.

We study the design tradeoffs involved in video streaming in networks with QoS guarantees. We approach this problem by using a utility function to quantify the benefit a user derives from the received video sequence. This benefit is expressed as a function of the total distortion. In addition, we also consider the cost, in network resources, of a video streaming system. The goal of the network user is then to obtain the most benefit for the smallest cost. We formulate this utility maximization problem as a joint constrained optimization problem. The difference between the utility and the network cost is maximized subject to the constraint that the decoder buffer does not underflow. We present a deterministic dynamic programming approach to find the optimal tradeoff for both the constant bit rate (CBR) and renegotiated constant bit rate (RCBR) service classes. Experimental results demonstrate the benefits and the performance of the proposed approach.