In video streaming over multicast network, user bandwidth requirement is often heterogeneous possibly with orders of magnitude difference (say, from hundreds of kb/s for mobile devices to tens of Mb/s for high-definition TV). Multiple descriptions coding (MDC) can be used to address this bandwidth heterogeneity issue. In MDC, the video source is encoded into multiple independent descriptions. A receiver, depending on its available bandwidth, joins different descriptions to meet their bandwidth requirements. An important but challenging problem for MDC video multicast is how to assign bandwidth to each description in order to maximize overall user satisfaction. In this paper,we investigate this issue by formulating it as an optimization problem, with the objective to maximize user bandwidth experience by taking into account the encoding inefficiency due to MDC.
We prove that the optimization problem is NP-hard. However, if the description number is larger than or equal to a certain threshold (e.g., if the minimum and maximum bandwidth requirements are 100 kb/s and 10 Mb/s, respectively, such threshold is seven descriptions), there is an exact and simple solution to achieve maximum user satisfaction, i.e., meeting all the bandwidth requirements. For the case when the description number is smaller, we present an efficient heuristic called simulated annealing for MDC bandwidth assignment (SAMBA) to assign bandwidth to each description given the distribution of user bandwidth requirement. We evaluate our algorithm using simulations. SAMBA achieves virtually the same optimal performance basedon exhaustive search. By comparing with other assignment algorithms, SAMBA significantly improves user satisfaction. We also show that, if the coding efficiency decreases with the number of descriptions, there is an optimal description number to achieve maximal user satisfaction.
In media streaming, the Internet’s 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.
- Source Partitioning
- Bandwidth Optimization
- Encodes Streaming Data