Adaptation of modulation and transmission bit-rates for video multicast in a multirate wireless network is a challenging problem because of network dynamics, variable video bit-rates, and heterogeneous clients who may expect differentiated video qualities. Prior work on the leader-based schemes selects the transmission bit-rate that provides reliable transmission for the node that experiences the worst channel condition.
However, this may penalize other nodes that can achieve a higher throughput by receiving at a higher rate. In this work, we investigate a rate-adaptive video multicast scheme that can provide heterogeneous clients differentiated visual qualities matching their channel conditions.
We first propose a rate scheduling model that selects the optimal transmission bit rates for each video frame to maximize the total visual quality for a multicast group subject to the minimum-visual-quality-guaranteed constraint. We then present a practical and easy-to-implement protocol, called QDM, which constructs a cluster-based structure to characterize node heterogeneity and adapts the transmission bit-rate to network dynamics based on video quality perceived by the representative cluster heads.
Since QDM selects the rate by a sample-based technique, it is suitable for real-time streaming even without any preprocess. We show that QDM can adapt to network dynamics and variable video-bit rates efficiently, and produce a gain of 2-5 dB in terms of the average video quality as compared to the leader-based approach. Quality-Differentiated Video Multicast in Multirate Wireless Networks
The wireless networks today with the ubiquitous of such applications, constant demand for better wireless access technology has resulted in several generations of new access point (AP) products, b/g/n, in a relatively short time. Future-generation APs are expected to have much greater computation capability and storage capacity. They offer new opportunities to incorporate even more advanced features to support a variety of applications in a new AP feature, namely quality-differentiated video multicast, to allow better utilization of limited wireless resources for video streaming. They measure the loss probability according to feedback acknowledged from the receiver, and predict a rate that can achieve the highest throughput. Such feedback-based schemes cannot be extended to multicast scenarios because concurrent feedback from several multicast members can lead to severe collision. To avoid this effect, some multicast rate adaptation schemes select the member who experiences the worst channel condition as the leader of the multicast group, and predict a bit rate that can reach this leader (the worst node). Such a leader-based approach is particularly suitable for applications that need to deliver data to all members reliably, e.g., data dissemination. However, this approach may not be efficient for video multicast because it merely selects the rate that maximizes the throughput of the worst node. In doing so, it penalizes those nodes who can receive data at a higher bit rate.
The Leader-Based Protocol (LBP) is the first leader-based approach proposed to overcome the problem of feedback collision. It selects the worst node as the leader to acknowledge multicast packets. Other members can issue negative acknowledgements to collide the acknowledgement sent by the leader and, thus, trigger the sender to retransmit the lost packets. The goal of LBP is to support reliability by a single feedback.
However, it does not adapt the transmission bit rate to dynamic channel conditions, but only sends data at the base rate. Thus, the rate adaptation algorithms, such as RAM and ARSM are proposed for the leader-based multicast protocol. The leader-based multicast protocol estimate link quality of the leader and determine a proper rate that can better reach the leader.
These techniques let each receiver embed the information about it receiving SNR value in the CTS frame. The sender can infer the leader’s SNR upon receiving the CTS frames, and predict a suitable rate accordingly. There are two states and two transition functions in the proposed two-state machine. We expect that the system stays in the active state if it still needs to search a suitable rate due to unstable environments.