For Real-time video broadcast where multiple users are interested in the same content, mobile-to-mobile cooperation can be utilized to improve delivery efficiency and reduce network utilization. Under such cooperation, however, real-time video transmission requires end-to-end delay bounds. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. For a scalable video structure, an alternative is to provide statistical guarantees using the concept of effective capacity/bandwidth by deriving quality of service exponents for each video layer. Using this concept, we formulate the resource allocation problem for general multi hop multicast network flows and derive the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. A method is described to compute the optimal resource allocation at each node in a distributed fashion. Furthermore, we propose low complexity approximation algorithms for energy-efficient flow selection from the set of directed acyclic graphs forming the candidate network flows. The flow selection and resource allocation process is adapted for each video frame according to the channel conditions on the network links. Considering different network topologies, results demonstrate that the proposed resource allocation and flow selection algorithms provide notable performance gains with small optimality gaps at a low computational cost.
In media streaming, the Internetís intrinsic heterogeneity continues a challenging problem. End users may have different edge bandwidth for data receiving or forwarding, especially in large-scale streaming with hundreds of thousands of users.Description coding rates have straightforward impact to the delivery performance. If a description has a high coding rate, some network paths may not have enough bandwidth to support its delivery. The loss rate of the description will be high. On the other hand, if descriptions have low coding rates, the number of descriptions and accordingly the coding cost will be high.
We propose an adaptive approach to adjust description coding rates according to the user bandwidth distribution.Our target is to provide the best streaming quality under certain network bandwidth constraint.We formulate the problem and address it by an adaptive solution. Our results show that arbitrary description rates may severely degrade system performance and an optimal solution can make significant improvement on the use of network bandwidth.
- Video Partitioning
- Effective Bandwidth Model
- Combinatorial Encoding of Network Flows