Globally, interactive streaming services including AR/VR streaming or cloud gaming are getting more and more attention. To support a smooth and natural experience during the service, a client’s actions should be quickly reflected on the screen in 20 ms while the video frames are generated from the server. To achieve this, the end-to-end latency should be shrunk as much as possible. While most of the works are focusing on reducing the encoding/decoding time or the volume of the video data at the application layer assuming the other layers are reliable, we try to give attention to the other layers. Considering that most streaming users prefer the use of wireless devices, it is important to study the performance of low latency streaming over wireless networks. In this work, we show that the use of general 802.11 (Wi-Fi) wireless networks might cause a long latency which impedes smooth and natural experience during extremely low latency streaming services. Especially with a poor wireless condition, the number of link-layer retransmissions increases resulting in long latency. To relieve the problem, we limit the number of link-layer retransmissions and complement its side-effect by using application layer forward error correction (AL-FEC). From the extensive evaluation on a real wireless testbed, we show that the proposed method could achieve constant low latency service even with a poor wireless condition.

Recently, with the rapid development of network and mobile device technologies, the number of services based on video streaming increased drastically. Most streaming applications such as YouTube, Netflix, and Twitch utilize large size buffers (over 1 s) to absorb sudden network dynamics. Before the buffer is depleted, lost packets are retransmitted, thus allowing users to enjoy watching video without recognizing network changes. However, some applications such as remote surgery and game streaming require extremely low end-to-end latency of less than 30 ms. We propose a streaming method suitable for such special streaming applications that need to transfer video with a very short latency. For this purpose, timeliness is paramount and we use UDP rather than TCP. To recover packet losses in a timely manner, we mainly utilize Forward Error Correction (FEC) rather than retransmissions that require time longer than round trip time. The proposed scheme consistently monitors real-time network conditions to decide the appropriate data rate and FEC parameters. Through extensive evaluations, we show that the proposed scheme efficiently achieves both timeliness and high robustness to packet losses.

Time Slotted Channel Hopping (TSCH), defined in the IEEE 802.15.4e standard requires complicated slot scheduling to enjoy its collision-free multihop communication capabilities fully. Recently, several autonomous scheduling algorithms that allocate slots without central control and additional control message exchange have been devised. However, autonomous algorithms are traffic oblivious, assigning the same number of slots regardless of traffic demands. Undifferentiated resource allotment is identified as the root cause that impairs the performance of TSCH networks. To address the issue, we propose A3, an autonomous and adaptive slot allocation scheme that adjusts the number of slots per slotframe according to varying traffic loads. A key component of A3 is receiver-side load estimation in real-time without any explicit control message exchange. A3 can be combined with any autonomous scheduling protocols, such as Orchestra and ALICE. With extensive evaluation on a large public testbed comprising 62 nodes, we verify that A3 significantly enhances autonomous scheduling algorithms' performance. It improves throughput by more than twice, PDR (Packet Delivery Ratio) by more than six times, and reduces the latency by ten times.

Although low-power lossy network (LLN), at its early stage, commonly used asynchronous link layer protocols for simple operation on resource-constrained nodes, development of embedded hardware and time synchronization technologies made Time-Slotted Channel Hopping (TSCH) viable in LLN (now part of IEEE 802.15.4e standard). TSCH has the potential to be a link layer solution for LLN due to its resilience to wireless interference (e.g., WiFi) and multi-path fading. However, its slotted operation incurs non-trivial cell scheduling overhead: two nodes should wake up at a time-frequency cell together to exchange a packet. Efficient cell scheduling in dynamic multihop topology in wireless environments has been an open issue, preventing TSCH's wide adoption in practice. This work introduces ALICE, a novel autonomous link-based cell scheduling scheme which allocates a unique cell for each directional link (a pair of nodes and traffic direction) by closely interacting with the routing layer and using only local information, without any additional communication overhead. We implement ALICE on Contiki and evaluate its effectiveness on the IoT-LAB public testbed with 68 nodes. ALICE generally outperforms Orchestra (the state-of-the-art method) and even more so under heavy traffic and high node density, increasing throughput by 2 times with 98.3% reliability and reducing latency by 70%, route changes by 95%, and radio duty cycle by 35%. ALICE can serve as an autonomous scheduling framework, which paves the way for TSCH-based LLN to go on.

Adaptive video streaming can provide adequate Quality of Experience by dynamically adjusting video rates in responding to fluctuating service conditions. However, adaptive video streaming shows dismal performances in wireless networks where several players share a wireless bottleneck link. This paper proposes a novel video rate selection scheme called XMAS for efficient video streaming in wireless networks. XMAS consists of two components: an available bandwidth estimation part and a video rate selection part. To redress the problem of inaccurate bandwidth estimation due to peculiar on-off transmission patterns of mobile video streaming, we devised a novel client-based traffic shaping scheme that effectively throttles server's packet transmission. Equipped with accurate bandwidth estimates, XMAS determines target transmission rates considering playback buffer levels. We implemented XMAS on Linux and performed rigorous experiments to analyze its behavior and performance. Our performance results showed that XMAS achieves up to 20% increase in average video rates while reducing rebuffer rates significantly.