This paper considers the design of cross-layer opportunistic transport protocols for stored video over wireless networks with a slow varying (average) capacity. We focus on two key principles: 1) scheduling data transmissions when capacity is high; and 2) exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, for example, users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules, which minimize system utilization (and, thus, possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand, we establish the optimal transmission schedule in a generalized piecewise constant thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online greedy fixed horizon control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. We also consider the multiuser case where one can exploit both future temporal and multiuser diversity. Finally, we investigate the impact of uncertainty in knowledge of future capacity variations, and propose an offline approach as well as an online algorithm to deal with such uncertainty. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.

This paper investigates how video-quality information can be exploited by HTTP-based adaptive streaming clients in their rate adaptation schemes. We also seek to answer the question: How much network coordination is required to achieve quality fairness across multiple competing clients? To that end, we envision a loosely coupled architecture where a lightweight centralized coordinator complements multiple federated quality-aware clients. Instead of actively programming network bandwidth allocation to each client, in our proposed architecture, the central coordinator simply collects quality and buffer level information from individual clients and publishes the aggregate quality statistics as reference. Such global reference statistics help to guide each federated client to self-tune its aggressiveness in its quality-aware adaptation scheme. Testbed-based evaluations show that such a lightweight approach is effective in reaping most of the performance gains previously achieved by a centralized joint optimization scheme. Compared to both state-of-the-art rate-based clients and independent quality-aware clients, our proposed federated quality-aware scheme can save up to 50% of total bandwidth while maintaining comparable aggregate video quality across all clients.

This paper explores how video quality information can be exploited by HTTP-based adaptive streaming (HAS) clients in their rate adaptation schemes. We first present an idealized combinatorial optimization formulation of the quality-aware rate adaptation problem and its corresponding solution. A few design principles are then introduced to arrive at a practical and robust rate adaptation scheme in the presence of real-world uncertainties (e.g., inaccuracy in bandwidth estimation, unforeseen changes in future video contents). Results from testbed-based experiments verify that by exploiting video quality information, our proposed quality-aware rate adaptation scheme significantly outperforms conventional rate-based clients in various contention-free and multi-client contention scenarios.

In this paper, we present an approach for joint rate allocation and quality selection for a novel video streaming scheme called streamloading. Streamloading is a recently developed method for delivering high-quality video without violating copyright enforced restrictions on content access for video streaming. In regular streaming services, content providers restrict the amount of viewable video that users can download prior to playback. This approach can cause inferior user experience due to bandwidth variations, especially in mobile networks with varying capacity. In streamloading, the video is encoded using scalable video coding, and users are allowed to pre-fetch enhancement layers and store them on the device, while base layers are streamed in a near real-time fashion ensuring that buffering constraints on viewable content are met. We begin by formulating the offline problem of jointly optimizing rate allocation and quality selection for streamloading in a wireless network. This motivates our proposed online algorithms for joint scheduling at the base station and segment quality selection at receivers. The results indicate that streamloading outperforms the state-of-the-art streaming schemes in terms of the number of additional streams we can admit for a given video quality. Furthermore, the quality adaptation mechanism of our proposed algorithm achieves a higher performance than baseline algorithms with no (or limited) video-centric optimization of the base station's allocation of resources, e.g., proportional fairness.

This paper considers the design of cross-layer opportunistic transport for stored video over wireless networks with a slow varying (average) capacity. We focus on two key ideas: (1) scheduling data transmissions when capacity is high; and (2), exploiting knowledge of future capacity variations. The latter is possible when users' mobility is known or predictable, e.g., users riding on public transportation or using navigation systems. We consider the design of cross-layer transmission schedules which minimize system utilization (and thus possibly transmit/receive energy) while avoiding, if at all possible, rebuffering/delays, in several scenarios. For the single-user anticipative case where all future capacity variations are known beforehand; we establish the optimal transmission schedule is a Generalized Piecewise Constant Thresholding (GPCT) scheme. For the single-user partially anticipative case where only a finite window of future capacity variations is known, we propose an online Greedy Fixed Horizon Control (GFHC). An upper bound on the competitive ratio of GFHC and GPCT is established showing how performance loss depends on the window size, receiver playback buffer, and capacity variability. Finally we consider the multiuser case where we can exploit both future temporal and multiuser diversity. Our simulations and evaluation based on a measured wireless capacity trace exhibit robust potential gains for our proposed transmission schemes.