Practical Recovery Solution for Information Loss in Real-Time Network Environment

Feedback mechanism based algorithms are frequently used to solve network optimization problems. These schemes involve users and network exchanging information (e.g. requests for bandwidth allocation and pricing) to achieve convergence towards an optimal solution. However, in the implementation, these algorithms do not guarantee that messages will be delivered to the destination when network congestion occurs. This in turn often results in packet drops, which may cause information loss, and this condition may lead to algorithm failing to converge. To prevent this failure, we propose least square (LS) estimation algorithm to recover the missing information when packets are dropped from the network. The simulation results involving several scenarios demonstrate that LS estimation can provide the convergence for feedback mechanism based algorithm

User Tolerance and Self-Regulation in Congestion Control

In response to poor quality of service (QoS), users self-regulate, i.e. they immediately release bandwidth and abandon network. However, there are studies that show users are willing to tolerate poor QoS for some time to evaluate if network performance will improve before abandoning the network. In this paper, we investigate how users willingness to wait for improved QoS may influence network activities, such as network pricing, bandwidth allocation, network revenue, and performance. We develop and employ a self-regulation model that includes user evaluation of QoS before deciding to abandon or stay in the network. This model considers these two factors: user tolerance of low QoS and the price per unit a user is willing to pay. Our investigation uncovers a double edged problem network may be populated with lower paying users, who are also dissatisfied. These lower paying users drive the price higher than the price produced by conventional solution for network congestion. This leads to our proposal for a market informed congestion control scheme, where network resolves congestion based on user profile that is defined by their ability to pay and demand for bandwidth.Comment: 9 page

QoE Support for Multi-Layered Multimedia Applications

Congestion control protocol and bandwidth allocation problems are often formulated into Network Utility Maximization (NUM) framework. Existing solutions for NUM generally focus on single-layered applications. As applications such as video streaming grow in importance and popularity, addressing user utility function for these multi-layered multimedia applications in NUM formulation becomes vital. In this paper, we propose a new multi-layered user utility model that leverages on studies of human visual perception and quality of experience (QoE) from the fields of computer graphics and human computer interaction (HCI). Using this new utility model to investigate network activities, we demonstrate that solving NUM with multi-layered utility is intractable, and that rate allocation and network pricing may oscillate due to user behavior specific to multi-layered applications. To address this, we propose a new approach for admission control to ensure quality of service (QoS) and quality of experience (QoE).Comment: 8 page