Providing real-time multimedia services over a besteffort network is challenging due to the stringent delay requirements in the presence of complex network dynamics. Multiple description (MD) coding is one approach to transmit the media over diverse (multiple) paths to reduce the detrimental effects caused by path failures or delay. The novelty of this work is to investigate the resource allocation in a network, where there are several competing MD coded streams. This is done by considering a framework that chooses the operating points for asymmetric MD coding to maximize total quality of the users, while these streams are sent over multiple routing paths. The framework is based on the theoretical modeling where we consider two descriptions and high source coding rate region approximated within small constants. We study the joint optimization of multimedia (source) coding and congestion control in wired networks. These ideas are extended to joint source coding and channel coding in wireless networks. In both situations, we propose distributed algorithms for optimal resource allocation. In the presence of path loss and competing users, the service quality to any particular MD stream could be uncertain. In such circumstances it might be tempting to expect that we need greater redundancy in the MD streams to protect against such failures. However, one surprising aspect of our study reveals that for large number of users who compete for the same resources, the overall system could benefit through opportunistic (hierarchical) strategies. In general networks, our studies indicate that the user composition varies from conservative to opportunistic operating points, depending on the number of users and their network vantage points.

The Peer-to-Peer (P2P) streaming has shown as an effective solution for wireline video applications, while for the wireless video streaming applications, the limited radio resource and battery energy are the main constraints on the way of P2P applications. An important issue in live video streaming quality of service is to avoid playback buffer underflow, and a challenge from wireless applications is the desire of energy efficiency. The problem we try to solve is how to utilize P2P schemes in video streaming and schedule the video transmission among peers to minimize the "freeze-ups" in playback caused by buffer underflow. In this work, we propose energy-efficient algorithm for the video transmission scheduling in wireless P2P live streaming system, to minimize the playback freeze-ups among peers. Further the algorithm is extended to two scenarios: peers' reluctance of consuming battery energy and allowing overhearing, with alternative energy-efficient algorithms proposed for the second scenario. Numerical results show the effectiveness of the proposed algorithms. The results also demonstrate that peers' selfishness may reduce the energy efficiency, but allowing overhearing could increase energy efficiency.

With the advances in wireless technology and explosive growth of mobile devices and wireless networks, mobile TV is becoming a popular application. The main technical challenge to wireless video broadcast is to provide the best quality of service possible under the radio resource constraints. In this paper we propose an application layer middleware solution that utilizes the scalability in video coding with rateless erasure correction codes to achieve a balance in the quality of service (QoS) and radio resource efficiency. Simulation results demonstrate the effectiveness of the solution.

Networks need to accommodate diverse applications with different quality-of-service (QoS) requirements. New ideas at the physical layer are being developed for this purpose, such as diversity embedded coding, which is a technique that combines high rates with high reliability. We address the problem of how to fully utilize different rate-reliability characteristics at the physical layer to support different types of traffic over a network and to jointly maximize their utilities. We set up a new framework based on utility maximization for networks with composite links, meaning that each link consists of sub-links that can attain different rate-reliability characteristics simultaneously. We incorporate delay, in addition to rate and reliability, into the utility functions. To accommodate different types of traffic, we propose distributed algorithms converging to the optimal rate-reliability-delay tradeoff based on capacity division and priority queueing. Numerical results show that compared with traditional codes, the new codes can provide higher network utilities for all traffic types simultaneously. The results also show that priority queueing achieves higher network utility than capacity division.

Internet is experiencing a substantial growth of video traffic. Given the limited network bandwidth resources, how to provide Internet users with good video playback quality-of-service (QoS) is a key problem. For video clips competing bandwidth, we propose an approach of Content-Aware distortion-Fair (CAF) video delivery scheme, which is aware of the characteristics of video frames and ensures max-min distortion-fair sharing among video flows. CAF leverages content-awareness to prioritize packet dropping during congestion. Different from bandwidth fair sharing, CAF targets end-to-end video playback quality fairness among users. The proposed CAF approach does not require rate-distortion modeling of the source, which is difficult to estimate. Instead, it exploits the temporal prediction structure of the video sequences along with a frame drop distortion metric to guide resource allocations and coordinations. Experimental results show that the proposed approach operates with limited overhead in computation and communication, and yields better QoS, especially when the network is congested.

Running multiple virtual networks, customized for different performance objectives, is a promising way to support diverse applications over a shared substrate. Despite being simple, a static division of resources between virtual networks can be highly inefficient, while dynamic resource allocation runs the risk of instability. This paper uses optimization theory to show that adaptive resource allocation can be stable and can maximize the aggregate performance across the virtual networks. In the DaVinci architecture, each substrate link periodically reassigns bandwidth shares between its virtual links; while at a smaller timescale, each virtual network runs a distributed protocol that maximizes its own performance objective independently. Numerical experiments with a mix of delay-sensitive and throughput-sensitive traffic show that the bandwidth shares converge quickly to the optimal values. We demonstrate that running several custom protocols in parallel and allocating resource adaptively can be more efficient, more flexible, and easier to manage than a compromise one-size-fits-all design.

Internet is experiencing an explosive growth of video traffic. Given the limited network bandwidth resources, how to provide Internet users with good video playback quality is a key problem. For video clips competing bandwidth, we propose an approach of content-aware distortion-fair (CAF) video delivery scheme, which is assumed to be aware of the characteristics of video frames and ensures max-min distortion fair sharing among video flows. Different from bandwidth fair sharing, CAF targets video playback quality fairness for the reason that users care about video quality rather than bandwidth. The proposed CAF approach does not need an analytical rate-distortion function which is difficult to estimate, but instead, it uses the explicit distortion of every frame which is induced by frame drop. Our CAF approach is fast and practical with content-aware cooperation. Experimental results show that the proposed approach yields better quality of service when the network is congested compared with the approach not rate-distortion optimized, and it makes competing video clips help each other to get fair playback quality.

To provide real-time multimedia services over a network is challenging due to the stringent delay requirements in the presence of complex network dynamics. Yet such services are beginning to be deployed over best effort networks. Multiple description (MD) coding is one approach to transmit the media over diverse (multiple) paths to reduce the detrimental effects caused by path failures or delay. The novelty of this work is to investigate the resource allocation in a network, where there are several competing MD coded streams. This is done by considering a framework that chooses the operating points for asymmetric MD coding to maximize total quality of the users, while these streams are sent over multiple routing paths. We study the joint optimization of multimedia (source) coding and congestion control in wired networks. These ideas are extended to joint source coding and channel coding in wireless networks. In both situations, we propose distributed algorithms for optimal resource allocation. In the presence of path loss and competing users, the service quality to any particular MD stream could be uncertain. In such circumstances it might be tempting to expect that greater redundancy in the MD streams is needed to protect against such failures. However, one surprising aspect of our study reveals that for large number of users competing for the same resources, the overall system could benefit through opportunistic (hierarchical) strategies. In general networks, our studies indicate that the user composition varies from conservative to opportunistic operating points, depending on the number of users and their network vantage points.

For Internet based video broadcasting applications such as IPTV, the peer-to-peer (P2P) streaming scheme has been found to be an effective solution. An important issue in live broadcasting is to avoid playback buffer underflow. How to utilize the playback buffer and upload bandwidth of peers to minimize the freeze-ups in playback, is the problem we try to solve. In this work, we propose a successive water-filling (SWaF) algorithm for the video transmission scheduling in P2P live streaming system, to minimize the playback freeze-ups among peers. SWaF algorithm only needs each peer to optimally transmit (within its uploading bandwidth) part of its available video segments in the buffer to other peers requiring the content and pass small amount message to some other peers. Moreover, SWaF has low complexity and provable optimality. Numerical results demonstrated the effectiveness of the proposed algorithm.

Service availability is one of the most closely scrutinized metrics in offering network services. It is important to cost- effectively provision a managed and differentiated network with various service availability guarantees under a unified platform. In particular, demands for availability may be elastic and such elasticity can be leveraged to improve cost-effectiveness. In this paper, we establish the framework of provisioning elastic service availability through network utility maximization, and propose an optimal and distributed solution using differentiated failure recovery schemes. First, we develop a utility function with configurable parameters to represent the satisfaction perceived by a user upon service availability as well as its allowed source rate. Second, adopting Quality of Protection [1] and shared path protection, we transform optimal provisioning of elastic service availability into a convex optimization problem. The desirable service availability and source rate for each user can be achieved using a price-based distributed algorithm. Finally, we numerically show the tradeoff between the throughput and the service availability obtained by users in various network topologies. This investigation quantifies several engineering implications. For example, indiscriminately provisioning service availabilities for different kinds of users within one network leads to noteworthy sub-optimality in total network utility. The profile of bandwidth usage also illustrates that provisioning high service availability exclusively for critical applications leads to significant waste in bandwidth resource.

We study the congestion control in a network where the users may have different types of traffic, such as the traffic with fixed/variable rate, delay sensitive/insensitive, etc. To reflect the different requirements on delay by different applications, explicit terms of delay are added to the utility function. We analyze the essential dynamics for the network utility maximization (NUM) with the new utility functions. Compared with the basic NUM where the utility function is only a function of rate, the dynamics for link price is now related to the delay term added in the utility function. The analysis is applied to the system with voice and data traffic, and distributed algorithms are proposed to allocate the resource such that the utility of voice and data is jointly optimized. The numerical results show that by the new price dynamics, we can accomplish optimal congestion control for users with delay sensitive/insensitive traffic in a network. In particular, in a network with data and voice traffic with priority queueing, the algorithm can lead the network to achieve higher quality of voice traffic and higher throughput of data traffic, with the sacrifice of the packet delay of data traffic.

Networks need to accommodate diverse applications with different quality-of-service (QoS) requirements. New ideas at the physical layer are being developed for this purpose, such as diversity embedded coding, which is a technique that combines high rates with high reliability. We address the problem of how to fully utilize different rate-reliability characteristics at the physical layer to support different types of traffic over a network and to jointly maximize their utilities. We set up a new framework based on utility maximization for networks with composite links, meaning that each link consists of sub-links that can attain different rate-reliability characteristics simultaneously. We incorporate delay, in addition to rate and reliability, into the utility functions. To accommodate different types of traffic, we propose distributed algorithms for the optimal rate-reliability-delay tradeoff based on capacity division and priority queueing. Numerical results show that compared with traditional codes, the new codes can provide higher network utilities for all traffic types simultaneously. The results also show that priority queueing achieves higher network utility than capacity division.

Service availability is one of the most closely scrutinized metrics in offering network services. The network vendor can earn more revenue from the customers by guaranteeing higher service availability at the cost of higher operational expense. It is important to cost-effectively provision a managed and differentiated network with various service availability guarantees under a unified platform. In this paper, we establish the framework of provisioning elastic service availability through network utility maximization, and propose an optimal and distributed solution using differentiated failure recovery schemes. First, we develop a utility function with configurable parameters to represent the satisfaction perceived by a user upon service availability as well as its allowed source rate. Second, adopting quality of protection [1] and shared path protection, we transform optimal provisioning of elastic service availability into a convex optimization problem. The desirable service availability and source rate for each user can be achieved using a price-based distributed algorithm. Finally, we numerically show the tradeoff between the throughput and the service availability obtained by users in various network topologies. Several quantitative observations are made from this investigation. For example, indiscriminately provisioning service availabilities for different kinds of users within one network leads to noteworthy sub-optimality in total network utility. The profile of bandwidth usage also illustrates that provisioning high service availability exclusively for critical applications leads to significant waste in bandwidth resource.