Robust Rate Adaptation Algorithm Research Articles

LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming

Video streaming today accounts for up to 55\% of mobile traffic. In this paper, we explore streaming videos encoded using Scalable Video Coding scheme (SVC) over highly variable bandwidth conditions such as cellular networks. SVC's unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel QoE metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop Layered Bin Packing (LBP) Adaptation Algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed including handling bandwidth prediction errors, and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared to the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today's mobile devices.

Understanding the Responsiveness of Open Loop Rate Adaptation Algorithms of IEEE 802.11 WLAN

EEE 802.11 WLAN can adjust transmission rates according to channel environment. Among rate adaptation methods, open loop schemes do not rely on explicit messages that tell channel state of receivers but can estimate from the arrival of ACKs. However they cause rates to alternate repeatedly. To remedy such oscillation, Adaptive Auto Rate Fallback (AARF) and Robust Rate Adaptation Algorithm Plus (RRAA+) were proposed. This paper evaluates their performance by using simulations in order to understand their characteristics and responsiveness to channel conditions. We expect such efforts to encourage new ideas to improve them further. From the evaluations, we observed that AARF is superior to RRAA+ in the perspective of throughput. Also, we found that the oscillation does not badly affect performance as known and TCP and UDP traffic are influenced differently by rate adaptation.

Toward History-Aware Robust 802.11 Rate Adaptation

Rate adaptation is a mechanism unspecified by the IEEE 802.11 standards, yet critical to the system performance by exploiting the multirate capability at the physical layer. In this paper, we conduct a systematic experimental study on rate adaptation over 802.11 wireless networks. Our key contributions are as follows: First, we present a critique on popular design guidelines adopted by many practical algorithms and we uncover their limitations. Our study reveals that these seemingly correct guidelines can be misleading in practice, thus incurring significant performance penalty in certain scenarios. Second, we study the short-term channel dynamics and explore how they guide rate adaptation. To this end, we design and implement a new History-Aware Robust Rate Adaptation Algorithm (HA-RRAA). HA-RRAA uses short-term loss ratio to opportunistically guide its rate change decisions, a cost-effective adaptive RTS filter to prevent collision losses from triggering rate decrease and an adaptive time window to limit transmissions at high loss rates. Our extensive experiments show that HA-RRAA outperforms popular algorithms in all tested scenarios, with goodput gains up to 51.9 percent in field trials.