Experimental evaluation of large scale WiFi multicast rate control

Abstract

Abstract-WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

Light-Weight Feedback Mechanism for WiFi Multicast to Very Large Groups—Experimental Evaluation

“…ensuring high Packet Delivery Ratio (PDR) for a large fraction of the nodes. Our recent work on multicast rate-adaption is given in [11]. (ii) Tuning FEC: We demonstrate in this paper that ensuring 100% packet deliveries to all nodes is challenging.…”

“…Nodes with P DR = 0 are active nodes that reported LQ values but unable to decode packets in the experiment run. For example, node (13,11) with LQ = 20 and P DR = 0 in Fig. 5(a) and 5(b) for a noise level at −70 dBm.…”

“…For instance, in Fig. 5(b) Node (7, 13) with PDR of 99% was selected as FB node although it has a neighbor, Node (7,11), with PDR of 80%. The reason is that Node (7, 11) is 6-adjacent to FB node (10,8) with PDR of 66%.…”

“…Each FB node, periodically sends an FB message which is roughly 100 bytes long. The frequency of feedback messages is applicationspecific e.g., for multicast rate adaptation application, 1s could be sufficient [11]. This implies that 50 FB nodes will add an overhead of 40Kbps.…”

PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc

“…Further, the authors use distributional reinforcement learning in [16] and evaluated their algorithm regarding the data rate of the broadcast messages and the reception success rate at STAs. In [17], the authors proposed MuDRA, a rate adaption algorithm for multicast flows. MuDRA relies on the information of the K STAs with the worst channel conditions, which the AP selects.…”