We present Akamai DNS, one of the largest authoritative DNS infrastructures in the world, that supports the Akamai content delivery network (CDN) as well as authoritative DNS hosting and DNS-based load balancing services for many enterprises. As the starting point for a significant fraction of the world's Internet interactions, Akamai DNS serves millions of queries each second and must be resilient to avoid disrupting myriad online services, scalable to meet the ever increasing volume of DNS queries, performant to prevent user-perceivable performance degradation, and reconfigurable to react quickly to shifts in network conditions and attacks. We outline the design principles and architecture used to achieve Akamai DNS's goals, relating the design choices to the system workload and quantifying the effectiveness of those designs. Further, we convey insights from operating the production system that are of value to the broader research community.

We consider video calls affected by bursty packet losses, where frame-level forward error correction (FEC) is employed due to delay constraints, and damaged frames and others predicted from them are discarded. Here, a high frame rate (FR) at low bitrates leads to large quantization step sizes (QS), small frames, and suboptimal FEC, whereas a low FR at high bitrates reduces the perceptual quality. To mitigate frame losses and freezing, hierarchical-P (hierP) temporal layering can be used with lower coding efficiency than IPPP coding. We study the received quality maximization for hierP and IPPP, by jointly optimizing the encoding FR, QS, and the FEC redundancy, under sending bitrate constraints. Building upon Q-STAR perceptual quality and R-STAR bitrate models, which depend on QS, and the decoded and encoding FR, respectively, we cast the problem as a combinatorial optimization problem. We solve for the encoding FR and the bitrate using exhaustive search, hill-climbing, and a greedy FEC distribution algorithm to determine the FEC redundancies. We show that, for random losses, the FEC bitrate ratio is an affine function of the packet-loss rate; low encoding FR is preferred at wider sending bitrate ranges with higher packet loss; layer protection is more even at higher bitrates; and IPPP, while achieving higher Q-STAR scores, is prone to freezing. For bursty losses, we show that layer redundancies are higher, rising with the mean burst length, reaching 80%; and hierP achieves higher Q-STAR scores than IPPP for longer bursts, and a smaller mean and variance of decoded frame distances.

360 degree video compression and delivery is one of the key components of virtual reality (VR) applications. In such applications, the users may freely control and navigate the captured 3D environment from any viewing direction. Given that only a small portion of the entire video is watched at any time, fetching the entire 360 degree raw video is therefore unnecessary and bandwidth-consuming. In this work, a novel two-tier 360 degree video streaming scheme is proposed to accommodate the dynamics in both network bandwidth and viewing direction. Based on the real-trace driven simulations, we demonstrate that the proposed framework can significantly outperform conventional 360 video streaming schemes.

We study interactive video calls between two users, where at least one of the users is connected over a cellular network. It is known that cellular links present highly-varying network bandwidth and packet delays. If the sending rate of the video call exceeds the available bandwidth, the video frames may be excessively delayed, destroying the interactivity of the video call. In this paper, we present Rebera, a cross-layer design of proactive congestion control, video encoding and rate adaptation, to maximize the video transmission rate while keeping the one-way frame delays sufficiently low. Rebera actively measures the available bandwidth in real-time by employing the video frames as packet trains. Using an online linear adaptive filter, Rebera makes a history-based prediction of the future capacity, and determines a bit budget for the video rate adaptation. Rebera uses the hierarchical-P video encoding structure to provide error resilience and to ease rate adaptation, while maintaining low encoding complexity and delay. Furthermore, Rebera decides in real time whether to send or discard an encoded frame, according to the budget, thereby preventing self-congestion and minimizing the packet delays. Our experiments with real cellular link traces demonstrate Rebera can, on average, deliver higher bandwidth utilization and shorter packet delays than Apple's FaceTime.

We consider peer-to-peer multi-party video conferencing (P2P-MPVC), where users with different uplink -downlink capacities send their videos using multicast trees. One way to deal with user bandwidth heterogeneity is employing layered video coding, generating multiple layers with different rates, whereas an alternative is partitioning the receivers of each source and disseminating a different non-layered video version within each group. In this paper, we aim to maximize the received video quality for both systems under uplink-downlink capacity constraints, while constraining the number of hops the packets traverse to two. We first show any multicast tree is equivalent to a collection of 1-hop and 2-hop trees, under user uplink-downlink capacity constraints. This reveals that the packet overlay hop count can be limited to two without sacrificing the achievable rate performance. Assuming a fine granularity scalable stream that can be truncated at any rate, we propose an algorithm that solves for the number of video layers, layer rates, and distribution trees for the layered system. For the partitioned simulcast system, we develop an algorithm to determine the receiver partitions along with the video rate and the distribution trees for each group. Through numerical comparison, we show that the partitioned simulcast system achieves the same average receiving quality as the ideal layered system without any coding overhead for the four-user systems simulated, and better quality than the layered system when the layered coding overhead is only 20%. The two systems perform similarly for the six-user case if the layered coding overhead is 10%.

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions .

In this paper, we present a pull-based dissemination protocol for efficient distribution of scalable video content in overlay peer-to-peer networks with mesh structures. The proposed protocol employs prioritized network coding, where the network coded packets belong to classes that represent packets of different priorities. For a receiver, the pull procedure begins with the reception of buffer vector messages from the senders, which bring information about the numbers and classes of available packets. The receiver node decides on the rate allocation of the different classes to be requested from each of the senders. The rate allocation is cast as a video quality maximization problem and solved using a hill-climbing algorithm. The simulation results show that the proposed mechanism, which is able to fully adapt to network dynamics, accounts for the unequal packet importances and utilizes the network resources efficiently.

Network coding has been recently proposed as an efficient method to improve throughput, minimize delays and remove the need for reconciliation between network nodes in distributed streaming systems. It permits to take advantage of the path and node diversity in the network when the network coding nodes are placed efficiently. In this paper, we investigate networks consisting of nodes that autonomously determine whether they should perform network coding or not as well as their set of parent nodes. Each node makes its decisions that maximize its quality of service. The decisions include the selection of operation mode (i.e., network coding mode, simple data forwarding mode) and the selection of extra connections. The resulting interactions among the nodes are modeled as a congestion game, thereby ensuring an equilibrium, i.e., stable multimedia stream flow. The experimental results show that the proposed scheme is appropriate for distributed multimedia transmission since it provides a stable quality without imposing centralized control.