Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

Zhuqi Li; Yaxiong Xie; Ravi Netravali; Kyle Jamieson

Home/Archive/arXiv

arXiv · 2204.12954

Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming

Zhuqi Li·Yaxiong Xie·Ravi Netravali·Kyle Jamieson

View original source ↗Read source document ↗Export metadata ↓

Abstract

Short video streaming applications have recently gained substantial traction, but the non-linear video presentation they afford swiping users fundamentally changes the problem of maximizing user quality of experience in the face of the vagaries of network throughput and user swipe timing. This paper describes the design and implementation of Dashlet, a system tailored for high quality of experience in short video streaming applications. With the insights we glean from an in-the-wild TikTok performance study and a user study focused on swipe patterns, Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages a simple, non machine learning-based model of users' swipe statistics to determine the pre-buffering order and bitrate. The net result is a system that achieves 77-99% of an oracle system's QoE and outperforms TikTok by 43.9-45.1x, while also reducing by 30% the number of bytes wasted on downloaded video that is never watched.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Zhuqi Li, Yaxiong Xie, Ravi Netravali, Kyle Jamieson. 2022-04-27. Dashlet: Taming Swipe Uncertainty for Robust Short Video Streaming. https://arxiv.org/abs/2204.12954

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Dynamic Content Caching with Waiting Costs via Restless Multi-Armed Bandits

We consider a system with a local cache connected to a backend server and an end user population. A set of contents are stored at the the server where they continuously get updated. The local cache keeps copies, potentially stale, of a subset of the contents. The users make content requests to the local cache which either can serve the local version if available or can fetch a fresh version or can wait for additional requests before fetching and serving a fresh version. Serving a stale version of a content incurs an age-of-version(AoV) dependent ageing cost, fetching it from the server incurs a fetching cost, and making a request wait incurs a per unit time waiting cost. We focus on the optimal actions subject to the cache capacity constraint at each decision epoch, aiming at minimizing the long term average cost. We pose the problem as a Restless Multi-armed Bandit(RMAB) Problem and propose a Whittle index based policy which is known to be asymptotically optimal. We explicitly characterize the Whittle indices. We numerically evaluate the proposed policy and also compare it to a greedy policy. We show that it is close to the optimal policy and substantially outperforms the exising policies.

cs.NI↗

arXiv2026

FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks

This paper introduces a forecasting-driven, incentive-aware service provisioning framework for distributed air--ground integrated networks with human--machine coexistence. Agent pairs (APs), each comprising a vehicle and its carried uncrewed aerial vehicles (UAVs), are proactively dispatched to overloaded hotspots to augment the computing capacity of edge servers (ESs). This design introduces four coupled challenges: uncertain spatio-temporal workloads, coupling between vehicular mobility and UAV capacity, forecast-driven contracting risks, and heterogeneous quality-of-service (QoS) requirements of human users (HUs) and machine users (MUs). To address these challenges, we propose FUSION, a two-stage framework with offline service preparation and online task scheduling. In the offline stage, a liquid neural network forecasts multi-step ES demand, an enhanced ant colony optimization scheme constructs AP service routes, and an auction-based mechanism establishes ES--AP contracts. In the online stage, we formulate congestion-aware scheduling as an exact-potential game among service demanders (SDs) and develop a potential-guided best-response dynamics algorithm. For a fixed online state, the algorithm converges to an $\varepsilon$-Nash equilibrium (NE) under a positive improvement threshold and to a pure-strategy NE when the threshold is zero. Within the considered contracting model, we theoretically establish that the offline mechanism satisfies individual rationality, near-truthfulness, and weak budget balance. Experiments on synthetic data and real-world load traces show that FUSION achieves higher social welfare while maintaining interaction delay and signaling energy overheads comparable to the considered benchmarks.

cs.NI↗

arXiv2026

Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6G

Emerging uplink-dominant 6G use cases, such as cooperative vehicular streaming, require efficient transmission of high-volume visual data over limited wireless resources. While semantic communications can reduce traffic by prioritizing task-relevant content, most existing approaches treat users independently and therefore overlook spatial redundancy among nearby devices' observations. This paper proposes a semantic-aware multiple access scheme that exploits overlapping fields of view among vehicular users to reduce redundant uplink transmissions. We formulate a joint perception and transmission control problem in which users decide which image patches to transmit, when to transmit them, and over which channel, subject to communication constraints. To address the resulting complexity, we introduce a practical two-phase approach. First, nearby vehicles share selected observation patches over Vehicle-to-Vehicle (V2V) links to calculate inter-user spatial redundancy. Second, users transmit only semantically important, non-redundant patches to the base station, where observations can be reconstructed using the received patches and complementary views from neighboring vehicles. Simulation results in a dense urban vehicular scenario demonstrate that our approach improves the proportion of users who achieve high-fidelity reconstruction, highlighting the potential of semantic-aware multiple access for sustainable and resource-efficient 6G uplink systems.

cs.NI↗