Optimal Bandwidth Assignment for Multiple-Description-Coded Video
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.
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
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• Mouse : Logitech.
• Ram : 256 Mb.
• Operating system : - Windows XP Professional.
• Coding Language : - Java.
• Tool Used : - Eclipse.
ALGORITHM & EXPLANATION
Using our algorithm, an optimal streaming rate and a set of optimal description rates could be computed. While the algorithm has been shown to be efficient through simulations, there are still many practical issues unaddressed. One challenge is how frequently the descriptions rates should be adjusted. If the network is highly dynamic, a highly frequent adjustment may better serve users. However, the cost for calculation would accordingly increase. We need to achieve proper tradeoff between the solution performance and the cost. Another challenge is to refine the problem formulation by considering very small descriptions. That is, some description rates from the optimal solution may be too low for practical MDC encoding. We should set a lower bound for the description coding rate, and prevent the algorithm from generating descriptions with lower rates than the bound.
- Source Partitioning.
- Bandwidth Optimization.
- Encodes Streaming Data.
The media source data will be converted into multiple streaming data. The original data will be partitioning into multiple streaming data for sets the descriptions. These partitions based on network bandwidth like its based on the users.
One traditional solution is to offer multi-rate video at the source side and to allow users to receive video data at different rates according to their respective bandwidth. MDC is one example of multi-rate video coding method. In MDC, data are encoded into several descriptions, which are independent of each other. When all the descriptions are received, the original data can be reconstructed without distortion. If only subsets of the descriptions are received, the quality of the reconstruction degrades gracefully. Therefore, in MDC, an end user can choose to receive the maximum number of descriptions under its edge bandwidth constraint.
Encodes Streaming Data
The source encodes streaming data into multiple descriptions. The number of descriptions and the coding rate of each description are computed by the source according to the network condition. In our formulation, we set some constraint on the user receiving rate but not user sending rate. Hence, an end user may fetch data from the source or from other users. Our model is applicable to both client/server networks and P2P networks. 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.
Pengye Xia, S.-H. Gary Chan, and Xing Jin, “Optimal Bandwidth Assignment for Multiple-Description-Coded Video”, IEEE Transaction on Multimedia, Vol. 13, No.2, April 2011.