Transport protocol for supporting multimedia streaming in mobile ad hoc networks has to cope with the rich dynamics, such as mobility-induced disconnection and reconnection, high out-of-order delivery ratios, channel errors, and network congestion. In this work, we design and implementate, ADTFRC, a TCP-friendly transport protocol for ad hoc networks. ADTFRC adapts wireline TFRC protocol to ad hoc networks with improved rate adaptation behavior, the capability of application layer framing and selective retransmission. ADTFRC detects different packet loss behaviors based on end-to-end measurements of multiple metrics. This allows ADTFRC to more accurately gauge the network behavior and achieve higher throughput. Simulations show that the performance of ADTFRC is higher than standard TFRC and TCP with explicit-link-failure-notification (ELFN) support in terms of throughput, rate adaptation behavior and application level quality while still maintaining the TCP-Friendliness property.

In this paper, we design a robust scheduling algorithm to ensure worst-case optimal performance when only coarse-grained channel state information (i.e., bounds on channel errors, but not the fine-grained error pattern) is available To solve this problem, we consider two coarse-grained channel error models and take a zero-sum game theoretic approach, in which the scheduler and the channel error act as non-cooperative adversaries in the scheduling process. Our results show that in the heavy channel error case, the optimal scheduler adopts a threshold form. It does not schedule a flow if the price (the flow is willing to pay) is too small, in order to maximize the system revenue. Among the scheduled flows, the scheduler schedules a flow with a probability inversely proportional to the flow price such that the risk of being caught by the channel error adversary is minimized. We also show that in the mild channel error model, the robust scheduling policy exhibits a balanced trade-off between a greedy decision and a conservative policy. The scheduler is likely to take a greedy decision if it evaluates the risk of encountering the channel error adversary now to be small. Therefore, robust scheduling does not always imply conservative decision. The scheduler is willing to take "risks" to expect higher gain in some scenarios. Our solution also shows that probabilistic scheduling may lead to higher worst-case performance compared to traditional deterministic policies. Finally, the current efforts show the feasibility to explore a probabilistic approach to cope with dynamic channel error conditions.