Resource Allocation for Downlink Multiuser Video Transmission Over Wireless Lossy Networks

Abstract

Demand for multimedia services, such as video streaming over wireless networks, has grown dramatically in recent years. The downlink transmission of multiple video sequences to multiple users over a shared resource-limited wireless channel, however, is a daunting task. Among the many challenges in this area are the time-varying channel conditions, limited available resources, such as bandwidth and power, and the different transmission requirements of different video content. This work takes into account the time-varying nature of the wireless channels, as well as the importance of individual video packets, to develop a cross-layer resource allocation and packet scheduling scheme for multiuser video streaming over lossy wireless packet access networks. Assuming that accurate channel feedback is not available at the scheduler, random channel losses combined with complex error concealment at the receiver make it impossible for the scheduler to determine the actual distortion of the sequence at the receiver. Therefore, the objective of the optimization is to minimize the expected distortion of the received sequence, where the expectation is calculated at the scheduler with respect to the packet loss probability in the channel. The expected distortion is used to order the packets in the transmission queue of each user, and then gradients of the expected distortion are used to efficiently allocate resources across users. Simulations show that the proposed scheme performs significantly better than a conventional content-independent scheme for video transmission.

Transmitting Important Bits and Sailing High Radio Waves: A Decentralized Cross-Layer Approach to Cooperative Video Transmission

“…5(d). 8 In the remainder of our experiments, we let the self-selection parameter ξ t = ξ = 0.2 because, as illustrated in Fig. 7 illustrates the activation frequencies for different relays and Fig.…”

“…This problem N. Mastronarde is further exacerbated in multi-user (MU) settings because they require multiple video streams, with heterogeneous traffic characteristics, to share the scarce wireless resources. To address these challenges, a lot of research has focused on MU wireless communication [1], [2], [3], [4], [5] and, in particular, MU video streaming over wireless networks [6], [7], [8], [9], [10]. The majority of this research relies on cross-layer adaptation to match available system resources (e.g., bandwidth, power, or transmission time) to application requirements (e.g., delay or source rate), and vice versa.…”

“…In MU video streaming applications [6], [7], [8], [9], [10], for example, cross-layer optimization is deployed to strike a balance between scheduling lucky users who experience very good fades, and serving users who have the highest priority video data to transmit. This tradeoff is important because rewarding a few lucky participants, as opportunistic multiple access policies do [2], [3], [4], does not translate to providing good quality to the application (APP) layer.…”

SSIM-Based Distortion Estimation for Optimized Video Transmission over Inherently Noisy Channels

“…The distortions of these reconstructed slices when compared with the original ones are then stochastically combined to provide the overall expected distortion. The IDE-based distortion is incorporated into the content-aware utility function proposed in (Maani, Pahalawatta, Berry, Pappas, & Katsaggelos, 2008) to perform packet scheduling and resource allocation for the transmission of video packets. For the second metric, Cumulative Distortion using SSIM, we develop a mechanism that also uses SSIM to measure the overall degradation in perceived quality due to a packet loss.…”

Delay-Constrained Video Transmission: A Power-Efficient Resource Allocation Approach for Guaranteed Perceptual Quality

“…In order to support transmission of real-time applications over wireless channels using Orthogonal Frequency Division Multiple Access (OFDMA), cross-layer techniques have been adopted in literature for dynamic resource allocation. However, the common practice is to maximize quality [3], throughput [5], [6] or energy efficiency [6], [7] or minimize expected distortion [8] or power consumption [9]- [11].…”